a controlled experiment two groups

• COVID-19 Tracker
• Biochemistry
• Anatomy & Physiology
• Microbiology
• Neuroscience
• Animal Kingdom
• NGSS High School
• Latest News
• Editors’ Picks
• Weekly Digest
• Quotes about Biology

Controlled Experiment

Reviewed by: BD Editors

Controlled Experiment Definition

A controlled experiment is a scientific test that is directly manipulated by a scientist, in order to test a single variable at a time. The variable being tested is the independent variable , and is adjusted to see the effects on the system being studied. The controlled variables are held constant to minimize or stabilize their effects on the subject. In biology, a controlled experiment often includes restricting the environment of the organism being studied. This is necessary to minimize the random effects of the environment and the many variables that exist in the wild.

In a controlled experiment, the study population is often divided into two groups. One group receives a change in a certain variable, while the other group receives a standard environment and conditions. This group is referred to as the control group , and allows for comparison with the other group, known as the experimental group . Many types of controls exist in various experiments, which are designed to ensure that the experiment worked, and to have a basis for comparison. In science, results are only accepted if it can be shown that they are statistically significant . Statisticians can use the difference between the control group and experimental group and the expected difference to determine if the experiment supports the hypothesis , or if the data was simply created by chance.

Examples of Controlled Experiment

Music preference in dogs.

Do dogs have a taste in music? You might have considered this, and science has too. Believe it or not, researchers have actually tested dog’s reactions to various music genres. To set up a controlled experiment like this, scientists had to consider the many variables that affect each dog during testing. The environment the dog is in when listening to music, the volume of the music, the presence of humans, and even the temperature were all variables that the researches had to consider.

In this case, the genre of the music was the independent variable. In other words, to see if dog’s change their behavior in response to different kinds of music, a controlled experiment had to limit the interaction of the other variables on the dogs. Usually, an experiment like this is carried out in the same location, with the same lighting, furniture, and conditions every time. This ensures that the dogs are not changing their behavior in response to the room. To make sure the dogs don’t react to humans or simply the noise of the music, no one else can be in the room and the music must be played at the same volume for each genre. Scientist will develop protocols for their experiment, which will ensure that many other variables are controlled.

This experiment could also split the dogs into two groups, only testing music on one group. The control group would be used to set a baseline behavior, and see how dogs behaved without music. The other group could then be observed and the differences in the group’s behavior could be analyzed. By rating behaviors on a quantitative scale, statistics can be used to analyze the difference in behavior, and see if it was large enough to be considered significant. This basic experiment was carried out on a large number of dogs, analyzing their behavior with a variety of different music genres. It was found that dogs do show more relaxed and calm behaviors when a specific type of music plays. Come to find out, dogs enjoy reggae the most.

Scurvy in Sailors

In the early 1700s, the world was a rapidly expanding place. Ships were being built and sent all over the world, carrying thousands and thousands of sailors. These sailors were mostly fed the cheapest diets possible, not only because it decreased the costs of goods, but also because fresh food is very hard to keep at sea. Today, we understand that lack of essential vitamins and nutrients can lead to severe deficiencies that manifest as disease. One of these diseases is scurvy.

Scurvy is caused by a simple vitamin C deficiency, but the effects can be brutal. Although early symptoms just include general feeling of weakness, the continued lack of vitamin C will lead to a breakdown of the blood cells and vessels that carry the blood. This results in blood leaking from the vessels. Eventually, people bleed to death internally and die. Before controlled experiments were commonplace, a simple physician decided to tackle the problem of scurvy. James Lind, of the Royal Navy, came up with a simple controlled experiment to find the best cure for scurvy.

He separated sailors with scurvy into various groups. He subjected them to the same controlled condition and gave them the same diet, except one item. Each group was subjected to a different treatment or remedy, taken with their food. Some of these remedies included barley water, cider and a regiment of oranges and lemons. This created the first clinical trial , or test of the effectiveness of certain treatments in a controlled experiment. Lind found that the oranges and lemons helped the sailors recover fast, and within a few years the Royal Navy had developed protocols for growing small leafy greens that contained high amounts of vitamin C to feed their sailors.

Related Biology Terms

• Field Experiment – An experiment conducted in nature, outside the bounds of total control.
• Independent Variable – The thing in an experiment being changed or manipulated by the experimenter to see effects on the subject.
• Controlled Variable – A thing that is normalized or standardized across an experiment, to remove it from having an effect on the subject being studied.
• Control Group – A group of subjects in an experiment that receive no independent variable, or a normalized amount, to provide comparison.

Cite This Article

Subscribe to our newsletter, privacy policy, terms of service, scholarship, latest posts, white blood cell, t cell immunity, satellite cells, embryonic stem cells, popular topics, digestive system, cellular respiration, scientific method, horticulture, hermaphrodite.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Control Groups and Treatment Groups | Uses & Examples

Control Groups and Treatment Groups | Uses & Examples

Published on July 3, 2020 by Lauren Thomas . Revised on June 22, 2023.

In a scientific study, a control group is used to establish causality by isolating the effect of an independent variable .

Here, researchers change the independent variable in the treatment group and keep it constant in the control group. Then they compare the results of these groups.

Using a control group means that any change in the dependent variable can be attributed to the independent variable. This helps avoid extraneous variables or confounding variables from impacting your work, as well as a few types of research bias , like omitted variable bias .

Table of contents

Control groups in experiments, control groups in non-experimental research, importance of control groups, other interesting articles, frequently asked questions about control groups.

Control groups are essential to experimental design . When researchers are interested in the impact of a new treatment, they randomly divide their study participants into at least two groups:

• The treatment group (also called the experimental group ) receives the treatment whose effect the researcher is interested in.
• The control group receives either no treatment, a standard treatment whose effect is already known, or a placebo (a fake treatment to control for placebo effect ).

The treatment is any independent variable manipulated by the experimenters, and its exact form depends on the type of research being performed. In a medical trial, it might be a new drug or therapy. In public policy studies, it could be a new social policy that some receive and not others.

In a well-designed experiment, all variables apart from the treatment should be kept constant between the two groups. This means researchers can correctly measure the entire effect of the treatment without interference from confounding variables .

• You pay the students in the treatment group for achieving high grades.
• Students in the control group do not receive any money.

Studies can also include more than one treatment or control group. Researchers might want to examine the impact of multiple treatments at once, or compare a new treatment to several alternatives currently available.

• The treatment group gets the new pill.
• Control group 1 gets an identical-looking sugar pill (a placebo)
• Control group 2 gets a pill already approved to treat high blood pressure

Since the only variable that differs between the three groups is the type of pill, any differences in average blood pressure between the three groups can be credited to the type of pill they received.

• The difference between the treatment group and control group 1 demonstrates the effectiveness of the pill as compared to no treatment.
• The difference between the treatment group and control group 2 shows whether the new pill improves on treatments already available on the market.

Prevent plagiarism. Run a free check.

Although control groups are more common in experimental research, they can be used in other types of research too. Researchers generally rely on non-experimental control groups in two cases: quasi-experimental or matching design.

Control groups in quasi-experimental design

While true experiments rely on random assignment to the treatment or control groups, quasi-experimental design uses some criterion other than randomization to assign people.

Often, these assignments are not controlled by researchers, but are pre-existing groups that have received different treatments. For example, researchers could study the effects of a new teaching method that was applied in some classes in a school but not others, or study the impact of a new policy that is implemented in one state but not in the neighboring state.

In these cases, the classes that did not use the new teaching method, or the state that did not implement the new policy, is the control group.

Control groups in matching design

In correlational research , matching represents a potential alternate option when you cannot use either true or quasi-experimental designs.

In matching designs, the researcher matches individuals who received the “treatment”, or independent variable under study, to others who did not–the control group.

Each member of the treatment group thus has a counterpart in the control group identical in every way possible outside of the treatment. This ensures that the treatment is the only source of potential differences in outcomes between the two groups.

Control groups help ensure the internal validity of your research. You might see a difference over time in your dependent variable in your treatment group. However, without a control group, it is difficult to know whether the change has arisen from the treatment. It is possible that the change is due to some other variables.

If you use a control group that is identical in every other way to the treatment group, you know that the treatment–the only difference between the two groups–must be what has caused the change.

For example, people often recover from illnesses or injuries over time regardless of whether they’ve received effective treatment or not. Thus, without a control group, it’s difficult to determine whether improvements in medical conditions come from a treatment or just the natural progression of time.

Risks from invalid control groups

If your control group differs from the treatment group in ways that you haven’t accounted for, your results may reflect the interference of confounding variables instead of your independent variable.

Minimizing this risk

A few methods can aid you in minimizing the risk from invalid control groups.

• Ensure that all potential confounding variables are accounted for , preferably through an experimental design if possible, since it is difficult to control for all the possible confounders outside of an experimental environment.
• Use double-blinding . This will prevent the members of each group from modifying their behavior based on whether they were placed in the treatment or control group, which could then lead to biased outcomes.
• Randomly assign your subjects into control and treatment groups. This method will allow you to not only minimize the differences between the two groups on confounding variables that you can directly observe, but also those you cannot.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Thomas, L. (2023, June 22). Control Groups and Treatment Groups | Uses & Examples. Scribbr. Retrieved September 3, 2024, from https://www.scribbr.com/methodology/control-group/

Is this article helpful?

Lauren Thomas

Other students also liked, what is a controlled experiment | definitions & examples, random assignment in experiments | introduction & examples, single, double, & triple blind study | definition & examples, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

• Science Notes Posts
• Contact Science Notes
• Todd Helmenstine Biography
• Anne Helmenstine Biography
• Free Printable Periodic Tables (PDF and PNG)
• Periodic Table Wallpapers
• Interactive Periodic Table
• Periodic Table Posters
• Science Experiments for Kids
• How to Grow Crystals
• Chemistry Projects
• Fire and Flames Projects
• Holiday Science
• Chemistry Problems With Answers
• Physics Problems
• Unit Conversion Example Problems
• Chemistry Worksheets
• Biology Worksheets
• Periodic Table Worksheets
• Physical Science Worksheets
• Science Lab Worksheets
• My Amazon Books

Control Group Definition and Examples

The control group is the set of subjects that does not receive the treatment in a study. In other words, it is the group where the independent variable is held constant. This is important because the control group is a baseline for measuring the effects of a treatment in an experiment or study. A controlled experiment is one which includes one or more control groups.

• The experimental group experiences a treatment or change in the independent variable. In contrast, the independent variable is constant in the control group.
• A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect.
• There are different types of control groups. A controlled experiment has one more control group.

Control Group vs Experimental Group

The only difference between the control group and experimental group is that subjects in the experimental group receive the treatment being studied, while participants in the control group do not. Otherwise, all other variables between the two groups are the same.

Control Group vs Control Variable

A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.

Types of Control Groups

There are different types of control groups:

• Placebo group : A placebo group receives a placebo , which is a fake treatment that resembles the treatment in every respect except for the active ingredient. Both the placebo and treatment may contain inactive ingredients that produce side effects. Without a placebo group, these effects might be attributed to the treatment.
• Positive control group : A positive control group has conditions that guarantee a positive test result. The positive control group demonstrates an experiment is capable of producing a positive result. Positive controls help researchers identify problems with an experiment.
• Negative control group : A negative control group consists of subjects that are not exposed to a treatment. For example, in an experiment looking at the effect of fertilizer on plant growth, the negative control group receives no fertilizer.
• Natural control group : A natural control group usually is a set of subjects who naturally differ from the experimental group. For example, if you compare the effects of a treatment on women who have had children, the natural control group includes women who have not had children. Non-smokers are a natural control group in comparison to smokers.
• Randomized control group : The subjects in a randomized control group are randomly selected from a larger pool of subjects. Often, subjects are randomly assigned to either the control or experimental group. Randomization reduces bias in an experiment. There are different methods of randomly assigning test subjects.

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

For example, consider a study of a new cancer drug. The experimental group receives the drug. The placebo group receives a placebo, which contains the same ingredients as the drug formulation, minus the active ingredient. The negative control group receives no treatment. The reason for including the negative group is because the placebo group experiences some level of placebo effect, which is a response to experiencing some form of false treatment.

Positive and Negative Controls

For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective.

The positive control group has a culture of bacteria that carry a drug resistance gene. If the bacteria survive drug exposure (as intended), then it shows the growth medium and conditions allow bacterial growth. If the positive control group dies, it indicates a problem with the experimental conditions. A negative control group of bacteria lacking drug resistance should die. If the negative control group survives, something is wrong with the experimental conditions.

• Bailey, R. A. (2008).  Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
• Chaplin, S. (2006). “The placebo response: an important part of treatment”.  Prescriber . 17 (5): 16–22. doi: 10.1002/psb.344
• Hinkelmann, Klaus; Kempthorne, Oscar (2008).  Design and Analysis of Experiments, Volume I: Introduction to Experimental Design  (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Pithon, M.M. (2013). “Importance of the control group in scientific research.” Dental Press J Orthod . 18 (6):13-14. doi: 10.1590/s2176-94512013000600003
• Stigler, Stephen M. (1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

Related Posts

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Controlled Experiments | Methods & Examples of Control

Controlled Experiments | Methods & Examples of Control

Published on 19 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

• Holding variables at a constant or restricted level (e.g., keeping room temperature fixed)
• Measuring variables to statistically control for them in your analyses
• Balancing variables across your experiment through randomisation (e.g., using a random order of tasks)

Table of contents

Why does control matter in experiments, methods of control, problems with controlled experiments, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables.

• Your independent variable is the colour used in advertising.
• Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

• Design and description of the meal
• Study environment (e.g., temperature or lighting)
• Participant’s frequency of buying fast food
• Participant’s familiarity with the specific fast food brand
• Participant’s socioeconomic status

Prevent plagiarism, run a free check.

You can control some variables by standardising your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., advert colour) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with colour blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment, and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

• A control group that’s presented with red advertisements for a fast food meal
• An experimental group that’s presented with green advertisements for the same fast food meal

Random assignment

To avoid systematic differences between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a ‘true experiment’ – it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers – or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs.

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses. In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity – the extent to which your results can be generalised to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritise control or generalisability in your experiment.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2022, October 10). Controlled Experiments | Methods & Examples of Control. Scribbr. Retrieved 3 September 2024, from https://www.scribbr.co.uk/research-methods/controlled-experiments/

Is this article helpful?

Pritha Bhandari

Understanding Control Groups for Research

Introduction

What are control groups in research, examples of control groups in research, control group vs. experimental group, types of control groups, control groups in non-experimental research.

A control group is typically thought of as the baseline in an experiment. In an experiment, clinical trial, or other sort of controlled study, there are at least two groups whose results are compared against each other.

The experimental group receives some sort of treatment, and their results are compared against those of the control group, which is not given the treatment. This is important to determine whether there is an identifiable causal relationship between the treatment and the resulting effects.

As intuitive as this may sound, there is an entire methodology that is useful to understanding the role of the control group in experimental research and as part of a broader concept in research. This article will examine the particulars of that methodology so you can design your research more rigorously .

Suppose that a friend or colleague of yours has a headache. You give them some over-the-counter medicine to relieve some of the pain. Shortly after they take the medicine, the pain is gone and they feel better. In casual settings, we can assume that it must be the medicine that was the cause of their headache going away.

In scientific research, however, we don't really know if the medicine made a difference or if the headache would have gone away on its own. Maybe in the time it took for the headache to go away, they ate or drank something that might have had an effect. Perhaps they had a quick nap that helped relieve the tension from the headache. Without rigorously exploring this phenomenon , any number of confounding factors exist that can make us question the actual efficacy of any particular treatment.

Experimental research relies on observing differences between the two groups by "controlling" the independent variable , or in the case of our example above, the medicine that is given or not given depending on the group. The dependent variable in this case is the change in how the person suffering the headache feels, and the difference between taking and not taking the medicine is evidence (or lack thereof) that the treatment is effective.

The catch is that, between the control group and other groups (typically called experimental groups), it's important to ensure that all other factors are the same or at least as similar as possible. Things such as age, fitness level, and even occupation can affect the likelihood someone has a headache and whether a certain medication is effective.

Faced with this dynamic, researchers try to make sure that participants in their control group and experimental group are as similar as possible to each other, with the only difference being the treatment they receive.

Experimental research is often associated with scientists in lab coats holding beakers containing liquids with funny colors. Clinical trials that deal with medical treatments rely primarily, if not exclusively, on experimental research designs involving comparisons between control and experimental groups.

However, many studies in the social sciences also employ some sort of experimental design which calls for the use of control groups. This type of research is useful when researchers are trying to confirm or challenge an existing notion or measure the difference in effects.

Workplace efficiency research

How might a company know if an employee training program is effective? They may decide to pilot the program to a small group of their employees before they implement the training to their entire workforce.

If they adopt an experimental design, they could compare results between an experimental group of workers who participate in the training program against a control group who continues as per usual without any additional training.

Qualitative data analysis starts with ATLAS.ti

Turn data into rich insights with our powerful data analysis software. Get started with a free trial.

Mental health research

Music certainly has profound effects on psychology, but what kind of music would be most effective for concentration? Here, a researcher might be interested in having participants in a control group perform a series of tasks in an environment with no background music, and participants in multiple experimental groups perform those same tasks with background music of different genres. The subsequent analysis could determine how well people perform with classical music, jazz music, or no music at all in the background.

Educational research

Suppose that you want to improve reading ability among elementary school students, and there is research on a particular teaching method that is associated with facilitating reading comprehension. How do you measure the effects of that teaching method?

A study could be conducted on two groups of otherwise equally proficient students to measure the difference in test scores. The teacher delivers the same instruction to the control group as they have to previous students, but they teach the experimental group using the new technique. A reading test after a certain amount of instruction could determine the extent of effectiveness of the new teaching method.

As you can see from the three examples above, experimental groups are the counterbalance to control groups. A control group offers an essential point of comparison. For an experimental study to be considered credible, it must establish a baseline against which novel research is conducted.

Researchers can determine the makeup of their experimental and control groups from their literature review . Remember that the objective of a review is to establish what is known about the object of inquiry and what is not known. Where experimental groups explore the unknown aspects of scientific knowledge, a control group is a sort of simulation of what would happen if the treatment or intervention was not administered. As a result, it will benefit researchers to have a foundational knowledge of the existing research to create a credible control group against which experimental results are compared, especially in terms of remaining sensitive to relevant participant characteristics that could confound the effects of your treatment or intervention so that you can appropriately distribute participants between the experimental and control groups.

There are multiple control groups to consider depending on the study you are looking to conduct. All of them are variations of the basic control group used to establish a baseline for experimental conditions.

No-treatment control group

This kind of control group is common when trying to establish the effects of an experimental treatment against the absence of treatment. This is arguably the most straightforward approach to an experimental design as it aims to directly demonstrate how a certain change in conditions produces an effect.

Placebo control group

In this case, the control group receives some sort of treatment under the exact same procedures as those in the experimental group. The only difference in this case is that the treatment in the placebo control group has already been judged to be ineffective, except that the research participants don't know that it is ineffective.

Placebo control groups (or negative control groups) are useful for allowing researchers to account for any psychological or affective factors that might impact the outcomes. The negative control group exists to explicitly eliminate factors other than changes in the independent variable conditions as causes of the effects experienced in the experimental group.

Positive control group

Contrasted with a no-treatment control group, a positive control group employs a treatment against which the treatment in the experimental group is compared. However, unlike in a placebo group, participants in a positive control group receive treatment that is known to have an effect.

If we were to use our first example of headache medicine, a researcher could compare results between medication that is commonly known as effective against the newer medication that the researcher thinks is more effective. Positive control groups are useful for validating experimental results when compared against familiar results.

Historical control group

Rather than study participants in control group conditions, researchers may employ existing data to create historical control groups. This form of control group is useful for examining changing conditions over time, particularly when incorporating past conditions that can't be replicated in the analysis.

Qualitative research more often relies on non-experimental research such as observations and interviews to examine phenomena in their natural environments. This sort of research is more suited for inductive and exploratory inquiries, not confirmatory studies meant to test or measure a phenomenon.

That said, the broader concept of a control group is still present in observational and interview research in the form of a comparison group. Comparison groups are used in qualitative research designs to show differences between phenomena, with the exception being that there is no baseline against which data is analyzed.

Comparison groups are useful when an experimental environment cannot produce results that would be applicable to real-world conditions. Research inquiries examining the social world face challenges of having too many variables to control, making observations and interviews across comparable groups more appropriate for data collection than clinical or sterile environments.

Analyze data and generate rich results with ATLAS.ti

Try out a free trial of ATLAS.ti to see how you can make the most of your qualitative data.

• Skip to secondary menu
• Skip to main content
• Skip to primary sidebar

Statistics By Jim

Making statistics intuitive

Control Group in an Experiment

By Jim Frost 3 Comments

A control group in an experiment does not receive the treatment. Instead, it serves as a comparison group for the treatments. Researchers compare the results of a treatment group to the control group to determine the effect size, also known as the treatment effect.

Imagine that a treatment group receives a vaccine and it has an infection rate of 10%. By itself, you don’t know if that’s an improvement. However, if you also have an unvaccinated control group with an infection rate of 20%, you know the vaccine improved the outcome by 10 percentage points.

By serving as a basis for comparison, the control group reveals the treatment’s effect.

Related post : Effect Sizes in Statistics

Using Control Groups in Experiments

Most experiments include a control group and at least one treatment group. In an ideal experiment, the subjects in all groups start with the same overall characteristics except that those in the treatment groups receive a treatment. When the groups are otherwise equivalent before treatment begins, you can attribute differences after the experiment to the treatments.

Randomized controlled trials (RCTs) assign subjects to the treatment and control groups randomly. This process helps ensure the groups are comparable when treatment begins. Consequently, treatment effects are the most likely cause for differences between groups at the end of the study. Statisticians consider RCTs to be the gold standard. To learn more about this process, read my post, Random Assignment in Experiments .

Observational studies either can’t use randomized groups or don’t use them because they’re too costly or problematic. In these studies, the characteristics of the control group might be different from the treatment groups at the start of the study, making it difficult to estimate the treatment effect accurately at the end. Case-Control studies are a specific type of observational study that uses a control group.

For these types of studies, analytical methods and design choices, such as regression analysis and matching, can help statistically mitigate confounding variables. Matching involves selecting participants with similar characteristics. For each participant in the treatment group, the researchers find a subject with comparable traits to include in the control group. To learn more about this type of study and matching, read my post, Observational Studies Explained .

Control groups are key way to increase the internal validity of an experiment. To learn more, read my post about internal and external validity .

Randomized versus non-randomized control groups are just several of the different types you can have. We’ll look at more kinds later!

Related posts : When to Use Regression Analysis

Example of a Control Group

Suppose we want to determine whether regular vitamin consumption affects the risk of dying. Our experiment has the following two experimental groups:

• Control group : Does not consume vitamin supplements
• Treatment group : Regularly consumes vitamin supplements.

In this experiment, we randomly assign subjects to the two groups. Because we use random assignment, the two groups start with similar characteristics, including healthy habits, physical attributes, medical conditions, and other factors affecting the outcome. The intentional introduction of vitamin supplements in the treatment group is the only systematic difference between the groups.

After the experiment is complete, we compare the death risk between the treatment and control groups. Because the groups started roughly equal, we can reasonably attribute differences in death risk at the end of the study to vitamin consumption. By having the control group as the basis of comparison, the effect of vitamin consumption becomes clear!

Types of Control Groups

Researchers can use different types of control groups in their experiments. Earlier, you learned about the random versus non-random kinds, but there are other variations. You can use various types depending on your research goals, constraints, and ethical issues, among other things.

Negative Control Group

The group introduces a condition that the researchers expect won’t have an effect. This group typically receives no treatment. These experiments compare the effectiveness of the experimental treatment to no treatment. For example, in a vaccine study, a negative control group does not get the vaccine.

Positive Control Group

Positive control groups typically receive a standard treatment that science has already proven effective. These groups serve as a benchmark for the performance of a conventional treatment. In this vein, experiments with positive control groups compare the effectiveness of a new treatment to a standard one.

For example, an old blood pressure medicine can be the treatment in a positive control group, while the treatment group receives the new, experimental blood pressure medicine. The researchers want to determine whether the new treatment is better than the previous treatment.

In these studies, subjects can still take the standard medication for their condition, a potentially critical ethics issue.

Placebo Control Group

Placebo control groups introduce a treatment lookalike that will not affect the outcome. Standard examples of placebos are sugar pills and saline solution injections instead of genuine medicine. The key is that the placebo looks like the actual treatment. Researchers use this approach when the recipients’ belief that they’re receiving the treatment might influence their outcomes. By using placebos, the experiment controls for these psychological benefits. The researchers want to determine whether the treatment performs better than the placebo effect.

Learn more about the Placebo Effect .

Blinded Control Groups

If the subject’s awareness of their group assignment might affect their outcomes, the researchers can use a blinded experimental design that does not tell participants their group membership. Typically, blinded control groups will receive placebos, as described above. In a double-blinded control group, both subjects and researchers don’t know group assignments.

Waitlist Control Group

When there is a waitlist to receive a new treatment, those on the waitlist can serve as a control group until they receive treatment. This type of design avoids ethical concerns about withholding a better treatment until the study finishes. This design can be a variation of a positive control group because the subjects might be using conventional medicines while on the waitlist.

Historical Control Group

When historical data for a comparison group exists, it can serve as a control group for an experiment. The group doesn’t exist in the study, but the researchers compare the treatment group to the existing data. For example, the researchers might have infection rate data for unvaccinated individuals to compare to the infection rate among the vaccinated participants in their study. This approach allows everyone in the experiment to receive the new treatment. However, differences in place, time, and other circumstances can reduce the value of these comparisons. In other words, other factors might account for the apparent effects.

Share this:

Reader Interactions

December 19, 2021 at 9:17 am

Thank you very much Jim for your quick and comprehensive feedback. Extremely helpful!! Regards, Arthur

December 17, 2021 at 4:46 pm

Thank you very much Jim, very interesting article.

Can I select a control group at the end of intervention/experiment? Currently I am managing a project in rural Cambodia in five villages, however I did not select any comparison/control site at the beginning. Since I know there are other villages which have not been exposed to any type of intervention, can i select them as a control site during my end-line data collection or it will not be a legitimate control? Thank you very much, Arthur

December 18, 2021 at 1:51 am

You might be able to use that approach, but it’s not ideal. The ideal is to have control groups defined at the beginning of the study. You can use the untreated villages as a type of historical control groups that I talk about in this article. Or, if they’re awaiting to receive the intervention, it might be akin to a waitlist control group.

If you go that route, you’ll need to consider whether there was some systematic reason why these villages have not received any intervention. For example, are the villages in question more remote? And, if there is a systematic reason, would that affect your outcome variable? More generally, are they systematically different? How well do the untreated villages represent your target population?

If you had selected control villages at the beginning, you’d have been better able to ensure there weren’t any systematic differences between the villages receiving interventions and those that didn’t.

If the villages that didn’t receive any interventions are systematically different, you’ll need to incorporate that into your interpretation of the results. Are they different in ways that affect the outcomes you’re measuring? Can those differences account for the difference in outcomes between the treated and untreated villages? Hopefully, you’d be able to measure those differences between untreated/treated villages.

So, yes, you can use that approach. It’s not perfect and there will potentially be more things for you to consider and factor into your conclusions. Despite these drawbacks, it’s possible that using a pseudo control group like that is better than not doing that because at least you can make comparisons to something. Otherwise, you won’t know whether the outcomes in the intervention villages represent an improvement! Just be aware of the extra considerations!

Best of luck with your research!

Comments and Questions Cancel reply

Experimental Design: Types, Examples & Methods

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Sweepstakes
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

Experimental Group in Psychology Experiments

In a randomized and controlled psychology experiment , the researchers are examining the impact of an experimental condition on a group of participants (does the independent variable 'X' cause a change in the dependent variable 'Y'?). To determine cause and effect, there must be at least two groups to compare, the experimental group and the control group.

The participants who are in the experimental condition are those who receive the treatment or intervention of interest. The data from their outcomes are collected and compared to the data from a group that did not receive the experimental treatment. The control group may have received no treatment at all, or they may have received a placebo treatment or the standard treatment in current practice.

Comparing the experimental group to the control group allows researchers to see how much of an impact the intervention had on the participants.

A Closer Look at Experimental Groups

Imagine that you want to do an experiment to determine if listening to music while working out can lead to greater weight loss. After getting together a group of participants, you randomly assign them to one of three groups. One group listens to upbeat music while working out, one group listens to relaxing music, and the third group listens to no music at all. All of the participants work out for the same amount of time and the same number of days each week.

In this experiment, the group of participants listening to no music while working out is the control group. They serve as a baseline with which to compare the performance of the other two groups. The other two groups in the experiment are the experimental groups.   They each receive some level of the independent variable, which in this case is listening to music while working out.

In this experiment, you find that the participants who listened to upbeat music experienced the greatest weight loss result, largely because those who listened to this type of music exercised with greater intensity than those in the other two groups. By comparing the results from your experimental groups with the results of the control group, you can more clearly see the impact of the independent variable.  

Some Things to Know

When it comes to using experimental groups in a psychology experiment, there are a few important things to know:

• In order to determine the impact of an independent variable, it is important to have at least two different treatment conditions. This usually involves using a control group that receives no treatment against an experimental group that receives the treatment. However, there can also be a number of different experimental groups in the same experiment.
• Care must be taken when assigning participants to groups. So how do researchers determine who is in the control group and who is in the experimental group? In an ideal situation, the researchers would use random assignment to place participants in groups. In random assignment, each individual stands an equal shot at being assigned to either group. Participants might be randomly assigned using methods such as a coin flip or a number draw. By using random assignment, researchers can help ensure that the groups are not unfairly stacked with people who share characteristics that might unfairly skew the results.
• Variables must be well-defined. Before you begin manipulating things in an experiment, you need to have very clear operational definitions in place. These definitions clearly explain what your variables are, including exactly how you are manipulating the independent variable and exactly how you are measuring the outcomes.

A Word From Verywell

Experiments play an important role in the research process and allow psychologists to investigate cause-and-effect relationships between different variables. Having one or more experimental groups allows researchers to vary different levels or types of the experimental variable and then compare the effects of these changes against a control group. The goal of this experimental manipulation is to gain a better understanding of the different factors that may have an impact on how people think, feel, and act.

Byrd-Bredbenner C, Wu F, Spaccarotella K, Quick V, Martin-Biggers J, Zhang Y. Systematic review of control groups in nutrition education intervention research . Int J Behav Nutr Phys Act. 2017;14(1):91. doi:10.1186/s12966-017-0546-3

Steingrimsdottir HS, Arntzen E. On the utility of within-participant research design when working with patients with neurocognitive disorders . Clin Interv Aging. 2015;10:1189-1200. doi:10.2147/CIA.S81868

Oberste M, Hartig P, Bloch W, et al. Control group paradigms in studies investigating acute effects of exercise on cognitive performance—An experiment on expectation-driven placebo effects . Front Hum Neurosci. 2017;11:600. doi:10.3389/fnhum.2017.00600

Kim H. Statistical notes for clinical researchers: Analysis of covariance (ANCOVA) . Restor Dent Endod . 2018;43(4):e43. doi:10.5395/rde.2018.43.e43

Bate S, Karp NA. A common control group — Optimising the experiment design to maximise sensitivity . PLoS ONE. 2014;9(12):e114872. doi:10.1371/journal.pone.0114872

Myers A, Hansen C. Experimental Psychology . 7th Ed. Cengage Learning; 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Understanding Simple vs Controlled Experiments

What Is a Simple Experiment? Controlled Experiment?

• Scientific Method
• Chemical Laws
• Periodic Table
• Projects & Experiments
• Biochemistry
• Physical Chemistry
• Medical Chemistry
• Chemistry In Everyday Life
• Famous Chemists
• Activities for Kids
• Abbreviations & Acronyms
• Weather & Climate
• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

An experiment is a scientific procedure used to test a hypothesis , answer a question, or prove a fact. Two common types of experiments are simple experiments and controlled experiments. Then, there are simple controlled experiments and more complex controlled experiments.

Simple Experiment

Although the phrase "simple experiment" is tossed around to refer to any easy experiment, it's actually a specific type of experiment. Usually, a simple experiment answers a "What would happen if...?" cause-and-effect type of question.

Example: You wonder whether a plant grows better if you mist it with water. You get a sense of how the plant is growing without being misted and then compare this with growth after you start misting it.

Why Conduct a Simple Experiment? Simple experiments usually provide quick answers. They can be used to design more complex experiments, typically requiring fewer resources. Sometimes simple experiments are the only type of experiment available, especially if only one sample exists.

We conduct simple experiments all the time. We ask and answers questions like, "Will this shampoo work better than the one I use?", "Is it okay to use margarine instead of butter in this recipe?", "If I mix these two colors, what will I get?"

Controlled Experiment

Controlled experiments have two groups of subjects. One group is the experimental group and it is exposed to your test. The other group is the control group , which is not exposed to the test. There are several methods of conducting a controlled experiment, but a simple controlled experiment is the most common. The simple controlled experiment has just the two groups: one exposed to the experimental condition and one not-exposed to it.

Example: You want to know whether a plant grows better if you mist it with water. You grow two plants. One you mist with water (your experimental group) and the other you don't mist with water (your control group).

Why Conduct a Controlled Experiment? The controlled experiment is considered a better experiment because it is harder for other factors to influence your results, which could lead you to draw an incorrect conclusion.

Parts of an Experiment

Experiments, no matter how simple or complex, share key factors in common.

• Hypothesis A hypothesis is a prediction of what you expect will happen in an experiment. It's easier to analyze your data and draw a conclusion if you phrase the hypothesis as an If-Then or cause and effect statement. For example, a hypothesis might be, "Watering plants with cold coffee will make them grow faster." or "Drinking cola after eating Mentos will cause your stomach to explode." You can test either of these hypotheses and gather conclusive data to support or discard a hypothesis. The null hypothesis or no-difference hypothesis is especially useful because it can be used to disprove a hypothesis. For example, if your hypothesis states, "Watering plants with coffee will not affect plant growth" yet if your plants die, experience stunted growth, or grow better, you can apply statistics to prove your hypothesis incorrect and imply a relationship between the coffee and plant growth does exist.
• Experimental Variables Every experiment has variables . The key variables are the independent and dependent variables . The independent variable is the one you control or change to test its effect on the dependent variable. The dependent variable depends on the independent variable. In an experiment to test whether cats prefer one color of cat food over another, you might state the null hypothesis, "Food color does not affect cat food intake." The color of the cat food (e.g., brown, neon pink, blue) would be your independent variable. The amount of cat food eaten would be the dependent variable. Hopefully, you can see how experimental design comes into play. If you offer 10 cats one color of cat food each day and measure how much is eaten by each cat you might get different results than if you put out three bowls of cat food and let the cats choose which bowl to use or you mixed the colors together and looked to see which remained after the meal.
• Data The numbers or observations you collect during an experiment are your data. Data are simply facts.
• Results Results are your analysis of the data. Any calculations you perform are included in the results section of a lab report.
• Conclusion You conclude whether to accept or reject your hypothesis. Usually, this is followed by an explanation of your reasons. Sometimes you may note other outcomes of the experiment, particularly those that warrant further study. For example, if you are testing colors of cat food and you notice the white areas of all the cats in the study turned pink, you might note this and devise a follow-up experiment to determine whether eating the pink cat food affects coat color.
• What Is a Controlled Experiment?
• What Is the Difference Between a Control Variable and Control Group?
• The Role of a Controlled Variable in an Experiment
• Scientific Variable
• DRY MIX Experiment Variables Acronym
• Scientific Method Vocabulary Terms
• Six Steps of the Scientific Method
• What Is an Experimental Constant?
• Null Hypothesis Examples
• What Is a Hypothesis? (Science)
• Random Error vs. Systematic Error
• What Are Examples of a Hypothesis?
• What Are the Elements of a Good Hypothesis?
• What Is a Testable Hypothesis?
• Scientific Method Flow Chart
• Scientific Hypothesis Examples

• Open access
• Published: 08 September 2024

Dietary processed former foodstuffs for broilers: impacts on growth performance, digestibility, hematobiochemical profiles and liver gene abundance

• Karthika Srikanthithasan 1 ,
• Marta Gariglio   ORCID: orcid.org/0000-0001-5224-8604 1 ,
• Elena Diaz Vicuna 1 ,
• Edoardo Fiorilla 1 ,
• Barbara Miniscalco 1 ,
• Valeria Zambotto 1 ,
• Eleonora Erika Cappone 1 ,
• Nadia Stoppani 1 ,
• Dominga Soglia 1 ,
• Federica Raspa 1 ,
• Joana Nery 1 ,
• Andrea Giorgino 1 ,
• Roser Sala 2 ,
• Andrés Luis Martínez Marínz 3 ,
• Josefa Madrid Sanchez 4 ,
• Achille Schiavone 1   na1 &
• Claudio Forte 1   na1  

Journal of Animal Science and Biotechnology volume  15 , Article number:  122 ( 2024 ) Cite this article

Metrics details

The present experiment aimed to evaluate the effects of commercially processed former foodstuffs (cFF) as dietary substitutes of corn, soybean meal and soybean oil on the growth performance, apparent total tract digestibility (ATTD), hematobiochemical profiles, and liver gene abundance in broiler chickens. Two hundred one-day-old male ROSS-308 chicks were assigned to 4 dietary groups (5 replicates of ten birds per replicate) according to their average body weight (BW, 38.0 ± 0.11 g). All groups received a two-phase feeding program: starter, d 1–12 and grower, d 12–33. The control group (cFF0) was fed a standard commercial feed based on corn, soybean meal and soybean oil. The other three groups received diets in which the feed based on corn, soybean meal, and soybean oil was partially replaced with cFF at a substitution level of 6.25% (cFF6.25), 12.5% (cFF12.5) or 25% (cFF25) for the following 33 d.

The growth performance data showed no differences in BW or average daily gain among groups, although the average daily feed intake decreased during the grower period (12–33 d) and over entire experimental period (1–33 d) in a linear manner as the cFF inclusion level rose ( P  = 0.026), positively affecting the gain to feed ratio ( P  = 0.001). The ATTD of dry matter of the cFF-fed groups were greater with respect to control group and increased throughout the experimental period, whereas the ATTD of ether extract linearly decreased with increasing levels of cFF-fed groups compared with control group and throughout the experimental period ( P  < 0.05). Additionally, a linear increase in the heterophil to lymphocyte ratio, serum cholesterol, triglycerides and alanine-aminotransferase were observed with increasing dietary levels of cFF ( P  < 0.05); however, no differences were observed in lipoprotein lipase or sterol regulatory element binding transcription factor gene abundance.

Conclusions

The results of this experiment demonstrate that it is possible to incorporate cFF into nutritionally balanced diets for broiler chickens, even up to 25% substitution levels, for up to 33 d without adversely impacting the overall growth performance of male broiler chickens raised under commercial conditions. Further studies are essential to validate the hematological trait findings.

As poultry diets are generally grain-based, the competition for resources between feed and food is of growing concern [ 1 ]. Ongoing research strives to identity sustainable alternatives to corn and soybean meal to replace their use in monogastric animal feed [ 2 ]. In 2022, the European Commission endorsed the use of former foodstuffs in livestock feed, defining them in the catalogue of feed materials [ 3 ] as: “ foodstuffs, other than catering reflux, which were manufactured for human consumption in full compliance with the EU food law, but which are no longer intended for human consumption for practical or logistical reasons or due to problems of manufacturing or packaging defects or other defects and which do not present any health risks when used as feed. ” Europe processes approximately 5 million tons of former foodstuffs annually [ 4 ]. These materials, which are legally distinct from food waste, offer a potential means to reduce the consumption of natural resources, such as water, reducing the carbon footprint and land usage of feed production [ 5 ].

In the last decade, former foodstuffs have been referred to in the literature as ‘ex food [ 6 ]’, ‘food leftovers [ 7 ]’, ‘former food products [ 8 ]’, ‘bakery by-products [ 9 ]’, and ‘bakery meal [ 10 ]’. With advancements in industrial processes, these former foodstuffs emerged as commercially processed former foodstuffs (cFF). They are composed of a mixture of different raw materials obtained from intermediate, unfinished, and incorrect products, primarily from the bakery, confectionary and food industries. These materials undergo unpacking, mixing, grinding, and drying to become feed ingredients as a commercialised product and available on the market under Commission Regulation (EU) 2022/1104 [ 3 ]. These cFF have a high energy content due to the presence of sugar, starch, oil, and fat [ 8 , 11 , 12 ]. The inclusion of cFF into animal feeds has proven to reduce food waste accumulation and dependency on traded feed [ 5 , 13 ].

Existing studies have shown that substituting traditional poultry feed components with cFF did not adversely affect broiler growth performance but decreased ileal digestible energy [ 14 , 15 ]. It is acknowledged that differences in source materials and processing techniques can lead to variations in the chemical composition and energy content of cFF [ 16 , 17 ]. Therefore, the involvement of an intermediate processor of former foodstuffs, able to standardize their composition and formulation as commercial feed, is crucial to ensure consistency among batches and provide a high-quality feed ingredient [ 12 ].

Despite the commercial availability of cFF and its recent standardization within the European legal framework, a substantial knowledge gap persists in the literature concerning its inclusion in poultry nutrition and its impact on growth performance, digestibility, liver gene abundance, and health [ 18 ]. Thus, the primary hypothesis of this experiment is that cFF, given its standardization as feed, can substitute traditional ingredients such as corn, soybean meal, and soybean oil in the broiler diet. Therefore, this experiment aimed to assess the impact of different dietary inclusion levels of cFF as a substitute for corn, soybean meal, and soybean oil in broiler diets, focusing on growth performance, apparent total tract digestibility (ATTD), hematobiochemical profiles, and liver gene abundance related to lipid metabolism.

Materials and methods

Bird management and experimental diets.

This experiment was conducted at the poultry facility of the University of Turin (North-West Italy). The experimental protocol was approved by the Bioethical Committee of the Department of Veterinary Sciences, University of Turin, Italy (protocol no. 245, 01/01/2022). A total of 200 one-day-old male Ross-308 broiler chicks were used in the 33-d experimental period. Chicks were individually weighed and divided into 4 dietary groups based on their initial body weight (BW; 38.0 ± 0.11 g, on average), each group comprised 5 pens of 10 chicks per pen. Each pen measured 1 m 2 and had an automatic ventilation system, rice hull litter, individual feeders and drinkers. For the initial three weeks, infrared lamps were used to maintain the temperature recommended for standard breeding practices, and the lighting schedule followed the Aviagen guidelines [ 19 ]. Chicks received vaccinations for Newcastle disease, Gumboro disease, infectious bronchitis and coccidiosis upon hatching. The birds and their environmental parameters were checked daily throughout the experimental period.

The cFF (PRIMO ® ) used in this experiment was provided by Dalma Mangimi SPA (Cuneo, Italy). The cFF’s ingredient list can be divided into 3 main categories, listed in decreasing order of relative quantity in the finished product: bakery by-products (such as wafers, biscuits, bread, crackers, snack, croissants, cakes, seasonal traditional desserts, breadsticks, sliced bread), former foodstuffs (dry pasta, chocolate, puffed cereals), and agro-industrial by-products (cocoa nib shells and hazelnut skins). These materials are primarily sourced through large-scale retail trade and pre-selling production phases, as defined by EU regulation 2022/1104. The cFF contains no other non-food ingredients, and specific recipes, unpacking methods, processing methodologies, and mixing information are protected under the patent rights of the producer.

Based on the cFF chemical composition (Table  1 ), four experimental diets were formulated for the two distinct feeding phases: starter (from d 1 to 12) and grower (from d 13 to 33), as shown in Table  2 . These diets were intended to meet the nutritional needs of broilers with nitrogen-corrected apparent metabolizable energy (AMEn, calculated) levels set at 3,000 kcal/kg for starter and 3,100 kcal/kg for grower phases, in accordance with National Research Council guidelines [ 20 ]. The control group received corn, soybean meal and soybean oil based standard commercial feed (cFF0). The other three experimental diets incorporated the cFF ingredient, substituting corn, soybean meal and soybean oil at the following percentages w/w: 6.25% (cFF6.25), 12.5% (cFF12.5), and 25% (cFF25) (Table  2 ). All diets were administered in crumble form (pelleting temperature 60 °C, size 0.4 cm, humidity 12%). Feed and water were provided ad libitum.

• Growth performance

At the beginning of the experiment, birds were individually labelled with a wing mark. The experimental period lasted 33 d, during which bird health status and mortality were monitored daily. The BW (g) of each bird was recorded upon its arrival and at the end of each feeding phase (1, 12, and 33 days of age, respectively). Feed intake (g) per replicate was recorded at the end of each feeding phase. The average daily gain (ADG, g/d), average daily feed intake (ADFI, g/d) and the gain to feed ratio (G:F, g/g) were calculated on the replicate basis for each feeding phase and for the whole experimental period (d 1–12, d 12–33, and d 1–33, respectively).

• Digestibility

The digestibility experiment was conducted at the end of each feeding phase. The indigestible marker titanium dioxide (TiO 2 ) (5 g/kg) was added to the feed during the formulation of the experimental diets (Table  2 ) to evaluate the ATTD. Excreta was collected according to the methods outlined by Dabbou et al. [ 21 ]. In brief, all birds of each replicate were removed from the pens and housed in wire-mesh cages (1 cage/replicate) to collect fresh excreta samples for approximately 1 h/d for 3 consecutive days. Following the collection, the excreta samples collected from each replicate over the 3 d were pooled and frozen at –20 °C until freeze-drying and analysis.

Feed and excreta chemical analyses

The feed, cFF ingredient, and dried excreta were analysed for dry matter (DM, 943.01), ash (942.05), crude protein (CP, 984.13), ether extract (EE, 2003.05), and crude fibre (978.10) using the standard methods outlined by the AOAC International [ 22 , 23 ]. The dietary starch and total sugar were determined for the cFF ingredient and all four diets, while the mineral contents were determined only for the cFF ingredient. All analyses were performed in accordance with the method specified in (EC) No 152/2009 [ 24 ]. To determine the amino acid content of cFF, samples underwent a 22-h hydrolysis step in 6 mol/L HCl at 112 °C under a nitrogen atmosphere. The amino acids in the hydrolysate were determined by HPLC (Waters Alliance System with a Waters 1525 Binary HPLC pump, Waters 2707 autosampler, and Waters 2475 multi λ Fluorescence Detector, Milford, USA) after derivatization, following the procedure described by Madrid et al. [ 25 ].

Excreta uric acid content was determined by spectrophotometry (UNICAN UV–Vis Spectrometry, Helios Gamma, UK) in accordance with the Marquardt method [ 26 ]. All analyses were performed on two replicates per sample. The CP amount in the excreta was corrected for uric acid nitrogen. The TiO 2  content of feed and freeze-dried excreta was assessed on a UV spectrophotometer (UNICAN UV–vis Spectrometry, Helios Gamma, UK) according to the method reported by Myers et al. [ 27 ]. The ATTD for DM, CP, and EE was calculated according to National Research Council guidelines [ 28 ].

The fatty acid profile of feed (% of total fatty acid methyl esters) was determined according to the methods described by Sukhija and Palmquist [ 29 ], and included saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), and total n-6 and total n-3 fatty acids. PUFA/SFA and n-6/n-3 ratios were calculated.

Hematobiochemical profiles

At the end of the experiment (d 33), three birds per replicate ( n  = 15 birds per dietary group) were slaughtered. Blood samples were collected from the jugular vein and 2.5 mL transferred into a EDTA tube and into a serum-separating tube. Blood smears were prepared from anticoagulant-free blood drops and stained using May-Grünwald and Giemsa stains [ 30 ]. Natt-Herrick solution-treated blood samples were used for total red and white blood cell counts using an improved Neubauer Hemacytometer [ 31 ]. One hundred leukocytes, comprising granular (heterophils, eosinophils, and basophils) and non-granular (lymphocytes and monocytes) types, were counted on each slide and expressed as a percentage of the total leukocytes. The heterophil to lymphocyte ratio was calculated according to Campbell [ 30 ].

Serum was obtained by allowing the anticoagulant-free tubes to stand at room temperature for approximately 2 h before centrifuging at 700 ×  g for 15 min. The resulting serum was frozen at –80 °C until further analysis. Total protein was quantified using the Biuret method (Bio Group Medical System kit), and the serum's electrophoretic pattern was obtained using a semi-automated agarose gel electrophoresis system (Sebia Hydrasys ® ). Enzymatic methods were used to measure alanine-aminotransferase, aspartate aminotransferase, gamma-glutamyl transferase, triglycerides, cholesterol, phosphorus, magnesium, iron, chloride, uric acid, and creatinine serum concentrations on a clinical chemistry analyser (Screen Master Touch, Hospitex diagnostics Srl., Firenze, Italy), as described by Salamano et al. [ 32 ].

Liver gene abundance analysis

On the day of slaughter (d 33), liver samples ( n  = 5 per dietary group) were taken from 20 broiler chickens and stored in RNAlater at –80 °C until RNA extraction. The nine liver genes analysed, involved in lipid and stress metabolism, were as follows: acyl-CoA oxidase-1 ( ACOX1 ), fatty acid binding protein-1 (FABP1), heat shock protein ( HSPA2 ), caspase-6 ( CASP6 ), catalase ( CAT ), fatty acid desaturase-2 ( FADS2 ), lipoprotein lipase ( LPL ), superoxide dismutase-1 ( SOD1 ), and sterol regulatory element binding transcription factor-2 ( SREBF2 ). Additionally, beta-actin ( ACTB ) and glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) were analysed as housekeeping genes. Total RNA was extracted using the FastGene ® RNA Premium Kit, and its quantity (Qbit ® , RNA Broad-Range Assay Kit) and integrity (RIN, Agilent 2,100 Bioanalyzer) analysed. Subsequently, all RNA was reverse-transcribed using the first strand cDNA synthesis kit, and cDNA was quantified using Qubit ® . A 1:50 dilution of cDNA was used to determine the appropriate concentration. For next-generation sequencing, library was prepared by purifying the multiplex polymerase chain reaction products (ExoSAP-IT ® Express), followed by index polymerase chain reaction. The products of the index polymerase chain reaction were quantified by Qubit ® and their sizes analysed using a bioanalyzer (Agilent 2100). Each library was diluted to 4 nmol/L and pooled for next-generation sequencing on the MiSeq Illumina platform. Multiplex digital expression gene analysis was performed by MiSeq Illumina [ 33 ].

Statistical analysis

Statistical analysis was performed using the IBM SPSS software package (version 21 for Windows, SPSS Inc., Chicago, IL, USA). Shapiro–Wilk’s test was used to establish data distributions. The assumption of equal variances was assessed using Levene’s test. Each replicate was considered an experimental unit in the evaluation of growth performance and digestibility ( n  = 5 replicates per dietary group), whereas the individual bird was used as the experimental unit for the analysis of blood parameters ( n  = 15 birds per dietary group). The collected data were analysed using one-way ANOVA. Polynomial contrasts were used to test the linear and quadratic responses to increased levels of cFF inclusion in the diet. Differences among dietary groups were considered statistically significant for P value ≤ 0.05. Results were expressed as means plus standard error of the mean (SEM).

R software (version 4.2.2) was used for statistical analysis of the gene abundance data. Read counts were performed using the package ‘featureCounts’; differential abundance gene analysis was conducted using the package ‘DESeq2 R’, with an adjusted P value < 0.05 as the threshold. Each dietary group was compared against all others to search for differential gene abundances. Differences among different experimental groups were visualised using the R package ‘Enhanced Volcano’ [ 34 ]. A principal component analysis (PCA) plot was generated to provide an overview of the differences in gene abundances among the dietary groups, visualized using R software.

Chemical composition of experimental diets

The chemical compositions of the cFF ingredient and the four experimental diets are summarized in Tables 1 and 2 , respectively. Starch content was similar among the 4 diets. As the inclusion level of cFF increased, the EE content of the experimental diets decreased, while total sugar increased. Fatty acids analysis (% of total fatty acid methyl esters) indicated a corresponding rise in SFA and MUFA content, as well as an increase in the n-6/n-3 ratio with increasing cFF inclusion level. Conversely, PUFA levels and the PUFA/SFA ratio decreased as cFF inclusion level increased.

No differences among dietary groups in terms of BW and ADG were registered across the whole experimental period (Table  3 ). Additionally, even if ADFI and G:F showed no differences during the starter feeding phase (d 1–12), a linear decrease ( P  < 0.05) in ADFI and a linear increase ( P  < 0.05) in G:F were observed during the grower phase (d 12–33) and the overall experimental period (d 1–33) for increasing cFF inclusion levels. A lower ADFI ( P  = 0.019) was noted in cFF25 compared with the other dietary groups during the grower period (d 12–33). In relation to the overall experimental period (d 1–33), feed consumption was 8% lower in the cFF25 group compared with the control group ( P  = 0.026).

Apparent total tract digestibility

The results of apparent total tract digestibility analysis are detailed in Table  4 , showing no differences among dietary groups in the ATTD of CP in either feeding phase. The ATTD of EE linearly decreased ( P  < 0.05) with increasing cFF level, whereas the ATTD of DM showed a linear increase ( P  < 0.05) with increasing cFF level throughout the two feeding phases. Notably, quadratic responses ( P  < 0.05) were observed during the starter period in the ATTD of EE and during the grower period in the ATTD of DM.

As outlined in Table  5 , the percentage of heterophils and the heterophil to lymphocyte ratio increased linearly ( P  < 0.001) as the cFF inclusion level increased, whereas the percentage of lymphocytes underwent a linear decrease ( P  = 0.001). Dietary treatment had no impact on monocytes, eosinophils, or basophils.

Considering the serum parameters (Table  5 ), we observed no differences in total protein level among dietary groups. By contrast, triglycerides and cholesterol exhibited a linear increase ( P  < 0.05) as the cFF inclusion level increased, with the highest values being observed in the cFF25 group. As for the analysis of serum minerals, iron, phosphorus, and chloride exhibited differences ( P  < 0.05) among dietary groups, whereas magnesium remained unaffected. In particular, levels of chloride and iron increased linearly ( P  < 0.05) with increasing cFF inclusion level. Indicators of liver function, including aspartate aminotransferase and gamma-glutamyl transferase, expressed no notable variations among dietary groups. Conversely, alanine-aminotransferase displayed a linear increase ( P  = 0.045) as cFF inclusion level increased, with the highest value observed in the cFF25 group. The inclusion of cFF in the diet had no effect on biomarkers of kidney function, namely creatinine and uric acid.

Liver gene abundance

The results of differential gene abundance analysis for each comparison are provided in the Additional file 1. The analysis showed no differences in gene abundances related to lipid metabolism (lipoprotein lipase and sterol regulatory element binding transcription factor) among the dietary groups, as demonstrated by principal component analysis (Fig.  1 ). However, the cFF6.25 group showed distinct abundance patterns in heat shock protein and caspase-6 with respect to the control and cFF12.5 groups, as illustrated in the volcano plot (Fig.  2 ). Specifically, the cFF6.25 group exhibited a down regulation ( P  = 0.008) of 2 genes (heat shock protein and caspase-6) related to stress and the immune system.

Gene abundance profiling; Principal component analysis. PC Principal component , cFF Commercially processed former foodstuffs , cFF0 Control diet (based on corn, soybean meal and soybean oil), cFF6.25 6.25% w/w substitution of corn, soybean meal and soybean oil with cFF, cFF12.5 12.5% w/w substitution of corn, soybean meal and soybean oil with cFF, cFF25 25% w/w substitution of corn, soybean meal and soybean oil with cFF

Gene abundance profiling; Volcano plot. cFF Commercially processed former foodstuffs , cFF0 Control diet (based on corn, soybean meal and soybean oil), cFF6.25 6.25% w/w substitution of corn, soybean meal and soybean oil with cFF, cFF12.5 12.5% w/w substitution of corn, soybean meal and soybean oil with cFF, cFF25 25% w/w substitution of corn, soybean meal and soybean oil with cFF), ACOX1 Acyl-CoA oxidase-1, FABP1 Fatty acid binding protein-1, HSPA2 Heat shock protein, CASP6 Caspase-6, CAT catalase, FADS2 Fatty acid desaturase-2, LPL Lipoprotein lipase, SOD1 Superoxide dismutase-1, SREBF2 Sterol regulatory element binding transcription factor-2, ACTB Beta-actin, GAPDH Glyceraldehyde-3-phosphate dehydrogenase

Defining nutritional and functional proprieties of cFF intended for animal nutrition is essential for standardising practices for their efficient use [ 12 ]. The literature contains limited in-depth research on the use of cFF in poultry nutrition. Hence, the aim of this experiment was to contribute towards filling this gap. One potential challenge associated with incorporating cFF into livestock diets is its variable composition, given that it consists of a mixture of diverse raw materials. However, advancements made to former foodstuffs processors in the feed industry mean that variations in cFF composition can now be predicted by considering the different types and relative quantities of raw materials involved. This experiment diverges from the existing literature through its use of a cFF ingredient with a standardized composition formula. It responds to the gap in the literature highlighted by Luciano et al. [ 8 ], who emphasized the significant challenge in formulating a standardized base feed using former foodstuffs.

EFFPA [ 13 ] illustrated similarities among the chemical composition of cFF and common cereals, whereas in vitro studies of pig nutrition [ 6 ] indicated cFF to have a higher glycaemic index potential than corn and heat-processed wheat. Dietary starch is one of the major energy sources for monogastric species, and effective starch digestion has a significant impact on the animals’ energy status [ 6 ]. Therefore, cFF has the potential to replace other energy-rich ingredients traditionally used in feed formulations, with positive effects in terms of the circularity of food production [ 9 ].

The findings of this experiment demonstrated that inclusion of different levels of cFF in the broiler diet was compatible with achieving growth performance in Ross-308 broilers when comparing cFF fed groups with the control group. While there were no differences in the BW among dietary groups, an improvement in G:F was observed in the groups fed cFF during the grower phase as well as across the overall experimental period compared with the control group. This improvement arises from differences observed in feed consumption among groups, while ADG resulted unaffected by the dietary groups. Our results align with studies [ 35 , 36 ] where high levels of dried bakery products, a type of former foodstuff, replaced corn and soybean meal in the broiler diet. Results of these experiments demonstrated that diets containing 25% or 30% dried bakery products had no adverse effects on growth performance compared with the control diet [ 35 ]. However, Potter et al. [ 37 ] observed a decrease in feed intake in turkeys fed a diet composed of 10% dried bakery products. The differences in the literature could be related to the ingredients contributing to the cFF and its chemical composition, or other aspects related to its processing methods.

Indeed, nutrient absorption can also be influenced by feed composition and the processing methods used. Ingredients like cereal flours, eggs, sugar and fats are typically mixed with water to create a dough or batter, which is then subjected to numerous technological processes that can improve their digestibility [ 11 ]. Cooking or thermal processing can alter the chemical and physical properties of food, impacting the access to and bioavailability of both macro- and micronutrients. However, the differences in growth performance observed between this experiment and the cited articles [ 35 , 36 ] could also be due to the differences in the chemical composition of the diet formulations, particularly in starch content and its digestibility [ 38 ]. The experiment by Abdollahi et al. [ 39 ] included feed processing variables in the broad spectrum of the factors able to affect feed intake. Svihus [ 40 ] stated that the much higher capacity of chickens to digest the starch component of feed ingredients (even native starch) compared with other species, such as pigs, rats, and humans, may be due to their particularly abundant secretion of amylolytic enzymes in the pancreatic juice. The author also mentioned that feed intake may be inversely correlated with starch digestibility [ 40 ].

In this experiment, the inclusion of increasing levels of cFF correlated with an increase in the ATTD of DM. This finding might be attributed to the balance between simple sugars and starch in the diet, which is related to the thermal processing of cFF, as reported by Luciano et al. [ 11 ]. Additionally, factors such as fatty acid chain length and degree of saturation can influence fat digestibility in poultry diets [ 41 ]. In this experiment, the ATTD of EE could have been influenced by the reduction in levels of soybean oil in the diet, which was substituted by cFF. This reduction, along with the dietary amount of EE and unsaturated fatty acids, decreased as the levels of cFF increased. However, the ATTD of apparent metabolizable energy, gross energy, and the digestibility of amino acids were not tested and require further investigation, which represents a limitation of this experiment.

The assessment of hematological parameters provides a convenient way to evaluate the nutritional and health status of animals over the course of a feeding experiment [ 38 ]. In this experiment, cFF inclusion increased the heterophil to lymphocyte ratio, which serves an indicator of the chicken’s immunological condition, inflammation status, and stress level, which are often attributed to dietary factors [ 42 ]. This experiment is the first to assess the effects of the dietary inclusion of cFF on the hematological traits of broilers. As dietary sugar levels increase with the inclusion of cFF, we might expect an increased heterophil to lymphocyte ratio, since a diet rich in simple carbohydrates is known to promote pro-inflammatory responses, as stated by Fajstova et al. [ 43 ]. Moreover, since cFF are primarily intended for human consumption, it is possible to speculate that the presence of gluten in cFF may have enhanced the inflammatory profile of serum, given the well-known pro-inflammatory action of gluten [ 44 ]. However, the gluten level of cFF was not assessed in this experiment. Additional studies on gut microbiota and histomorphology are needed to confirm the extent of this inflammatory condition in the birds fed diets incorporating cFF.

Serum analysis also indicated an impact of the cFF diet on serum lipidic metabolites. The cFF25-fed group exhibited the highest values compared with the control diet, consistent with previous findings linking diets rich in saturated fats to elevated blood cholesterol levels [ 45 , 46 ]. According to Velasco et al. [ 47 ], the source of dietary fat can impact the lipid profile of serum. In general, fats with a high concentration of SFA were found to elevate blood triacylglycerol levels, although the experiment also observed variations in the serum lipid concentrations in chickens which depended on the degree to which the sources of dietary fat were saturated.

The incorporation of cFF in the broiler diet also resulted in a noteworthy elevation of serum mineral concentrations, including phosphorus, iron, and chloride. The observed alterations may be linked to specific properties of the constituent components of cFF, which may have been subjected to mineral fortification, including high-salt products [ 48 ]. Whereas no differences in hepatic activity were revealed among dietary groups, as confirmed by aspartate aminotransferase and gamma-glutamyl transferase concentrations, the inclusion of cFF did lead to an increase in alanine-aminotransferase concentrations, which were nonetheless within the physiological range for broilers [ 49 ]. Importantly, no differences were noted in renal metabolites, with the results for creatinine indicating cFF inclusion to have no effect on kidney function in broilers. However, whereas the analysis of gene abundance related to lipid metabolism revealed no effect of the cFF diet on gene abundance at the hepatic level, 2 key genes involved in the stress response and the immune system were highlighted, namely heat shock protein and caspase-6. Under conditions of stress, heat shock protein gene abundance increases, playing a crucial role in protecting the body from oxidative stress [ 50 ]. Existing literature [ 50 ] suggests that the dietary intake of nutrients characterised by antioxidant properties might decrease the heat shock protein gene abundance. Additionally, elevated levels of caspase-6 gene have been associated with liver damage. Therefore, the lower abundance levels of these genes observed in cFF6.25-fed chickens may indicate an antioxidant activity of the ingredient and a subsequent reduction in liver stress [ 50 ]. Further investigations will be necessary to explore this possibility in depth.

To the best of the authors’ knowledge, this is the first experiment that has been conducted to perform a detailed evaluation of growth performance, nutrient digestibility, hematobiochemical profiles and liver gene abundance with the inclusion of cFF (up to 25%) in the broiler diets. The results demonstrated that, although BW and ADG exhibited no differences among experimental groups, a notable increase in G:F was recorded, providing insights into the potential for cFF to become an alternative ingredient in poultry nutrition. The observed increase in ATTD of DM with increasing substitution level concur with the increase in G:F, indicating a higher digestibility of feeds containing cFF, probably due to the higher level of processing of the raw materials, originally intended for human consumption.

The differences in the serum lipid profiles between control group birds and those receiving the cFF ingredient are also worth noting. Although all values remained within the physiological ranges for broilers, the changes associated with cFF inclusion in the diet should be taken into careful consideration. It is known that industrial baked sweets often contain considerable amounts of SFA and MUFA derived from butter or margarine. Therefore, the specific selection of raw materials, choosing from both sweet and savoury varieties, may modify the fatty acid profile of cFF. The composition of cFF should be carefully formulated to make it compatible with chicken metabolism and nutrition, thus avoiding adverse changes in the blood lipid profile which could affect liver metabolism and meat quality.

Further studies are essential to validate the hematological trait findings. Nonetheless, the findings presented here suggest that the incorporation of cFF into nutritionally balanced diets, even at levels as high as 25%, does not adversely impact the overall growth performance of male broiler chickens raised until 33 days of age under commercial conditions.

Availability of data and materials

The analysed data from this experiment are available upon request from the corresponding author.

Abbreviations

Average daily gain

Average daily feed intake

Body weight

Complementary deoxyribonucleic acid

Commercially processed former foodstuffs

Crude protein

Ether extract

Gain to feed ratio

Monounsaturated fatty acids

Polyunsaturated fatty acids

Ribonucleic acid

Standard error of the mean

Saturated fatty acids

Titanium dioxide

Breewood H, Garnett T. What is feed-food competition? FCRN. 2020. https://www.doi.org/10.56661/dde79ca0 . Accessed 2022 Mar 10.

Alshelmani MI, Abdalla EA, Kaka U, Basit MA. Nontraditional feedstuffs as an alternative in poultry feed. In: Patra AK, editor. Advances in poultry nutrition research. London: IntechOpen; 2021. https://doi.org/10.5772/intechopen.95946 .

European commission. Commission regulation (EU) 2022/1104 of 1 July 2022 amending regulation (EU) No 68/2013 on the catalogue of feed materials - strada lex Europe. Official Journal of the European Union. 2022. https://www.stradalex.eu/en/se_src_publ_leg_eur_jo/toc/leg_eur_jo_3_20220704_177/doc/ojeu_2022.177.01.0004.01 . Accessed 2023 May 15.

EFFPA (European Former Foodstuff Processor's Association). Animal husbandry and circular economy: the importance of Former foodstuff. EFFPA. 2022. https://www.effpa.eu/european-livestock-voice-animal-husbandryand-circular-economy-the-importance-of-former-foodstuff/ . Accessed 2023 May 15.

Pinotti L, Mazzoleni S, Moradei A, Lin P, Luciano A. Effects of alternative feed ingredients on red meat quality: a review of algae, insects, agro-industrial by-products and former food products. Ital J Anim Sci. 2023;22:695–710. https://doi.org/10.1080/1828051X.2023.2238784 .

Article   CAS   Google Scholar  

Ottoboni M, Tretola M, Luciano A, Giuberti G, Gallo A, Pinotti L. Carbohydrate digestion and predicted glycemic index of bakery/confectionary ex-food intended for pig nutrition. Ital J Anim Sci. 2019;18:838–49. https://doi.org/10.1080/1828051X.2019.1596758 .

Tretola M, Ferrari L, Luciano A, Mazzoleni S, Rovere N, Fumagalli F, et al. Sugary vs salty food industry leftovers in postweaning piglets: effects on gut microbiota and intestinal volatile fatty acid production. Animal. 2022;16:100584. https://doi.org/10.1016/j.animal.2022.100584 .

Article   CAS   PubMed   Google Scholar  

Luciano A, Tretola M, Ottoboni M, Baldi A, Cattaneo D, Pinotti L. Potentials and challenges of former food products (food leftover) as alternative feed ingredients. Animals. 2020;10:125. https://doi.org/10.3390/ani10010125 .

Article   PubMed   PubMed Central   Google Scholar  

Kaltenegger A, Humer E, Stauder A, Zebeli Q. Feeding of bakery by-products in the replacement of grains enhanced milk performance, modulated blood metabolic profile, and lowered the risk of rumen acidosis in dairy cows. J Dairy Sci. 2020;103:10122–35. https://doi.org/10.3168/jds.2020-18425 .

Stein HH, Adeola O, Baidoo SK, Lindemann MD, Adedokun SA, North Central Coordinating Committee on Swine Nutrition (NCCC-42). Standardized ileal digestibility of amino acids differs among sources of bakery meal when fed to growing pigs. J Anim Sci. 2023;101:skad208. https://doi.org/10.1093/jas/skad208 .

Luciano A, Tretola M, Mazzoleni S, Rovere N, Fumagalli F, Ferrari L, et al. Sweet vs. salty former food products in post-weaning piglets: effects on growth, apparent total tract digestibility and blood metabolites. Animals. 2021;11:3315. https://doi.org/10.3390/ani11113315 .

Pinotti L, Luciano A, Ottoboni M, Manoni M, Ferrari L, Marchis D, et al. Recycling food leftovers in feed as opportunity to increase the sustainability of livestock production. J Clean Prod Elsevier Ltd. 2021;294:126290. https://doi.org/10.1016/j.jclepro.2021.126290 .

Article   Google Scholar  

EFFPA [European Former Foodstuff Processor's Association]. What are former foodstuffs? | EFFPA. 2019. https://www.effpa.eu/what-are-former-foodstuffs/ . Accessed 2022 Mar 10.

Zhang F, Adeola O. Energy values of canola meal, cottonseed meal, bakery meal, and peanut flour meal for broiler chickens determined using the regression method. Poult Sci. 2017;96:397–404. https://doi.org/10.3382/ps/pew239 .

Stefanello C, Vieira SL, Xue P, Ajuwon KM, Adeola O. Age-related energy values of bakery meal for broiler chickens determined using the regression method. Poult Sci. 2016;95:1582–90. https://doi.org/10.3382/ps/pew046 .

Slominski BA, Boros D, Campbell LD, Guenter W, Jones O. Wheat by-products in poultry nutrition. Part I. chemical and nutritive composition of wheat screenings, bakery by-products and wheat mill run. Canadian J Animal Sci. 2004;84:421–8. https://doi.org/10.4141/A03-112 .

Liu Y, Jha R, Stein HH, Kim SW. Nutritional composition, gross energy concentration, and in vitro digestibility of dry matter in 46 sources of bakery meals. J Animal Sci. 2018;96:4685–92. https://doi.org/10.1093/jas/sky310 .

Srikanthithasan K, Giorgino A, Fiorilla E, Ozella L, Gariglio M, Schiavone A, et al. Former foodstuffs in feed: a minireview of recent findings. Environ Sci Pollut Res. 2024;31(16):23322–33. https://doi.org/10.1007/s11356-024-32695-2 .

Aviagen Ross broiler management. Ross | Aviagen. 2018. https://aviagen.com/assets/Tech_Center/Ross_Broiler/Ross-BroilerHandbook2018-EN.pdf . Accessed 2024 Apr 18.

National Research Council (NRC). Nutrient requirements of poultry. 9th ed. Washington: The National Academies Press; 1994.

Google Scholar  

Dabbou S, Schiavone A, Gai F, Martinez S, Madrid J, Hernandez F, et al. Effect of dietary globin, a natural emulsifier, on the growth performance and digestive efficiency of broiler chickens. Ital J Anim Sci. 2019;18:530–7. https://doi.org/10.1080/1828051X.2018.1547127 .

AOAC [Association of Official Analytical Chemists] International. Official methods of analysis of AOAC international. 16th ed. Gaithersburg: AOAC International; 2000.

AOAC [Association of Official Analytical Chemists] International. Official methods of analysis of AOAC international. 17th ed. Gaithersburg: AOAC International; 2003.

European commission. Commission regulation (EC) No 152/2009 of 27 January 2009 laying down the methods of sampling and analysis for the official control of feed. Official Journal of the European Union. 2009. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32009R0152 . Accessed 2024 June 20.

Madrid J, Martínez S, López C, Orengo J, López MJ, Hernández F. Effects of low protein diets on growth performance, carcass traits and ammonia emission of barrows and gilts. Anim Prod Sci. 2012;53:146–53. https://doi.org/10.1071/AN12067 .

Marquardt RR. A simple spectrophotometric method for the direct determination of uric acid in avian excreta. Poult Sci. 1983;62:2106–8. https://doi.org/10.3382/ps.0622106 .

Myers WD, Ludden PA, Nayigihugu V, Hess BW. Technical Note: A procedure for the preparation and quantitative analysis of samples for titanium dioxide. J Anim Sci. 2004;82:179–83. https://doi.org/10.2527/2004.821179x .

National Research Council (NRC). Nutrient requirements of swine. 11th ed. Washington: The National Academies Press; 2012.

Sukhija PS, Palmquist DL. Rapid method for determination of total fatty acid content and composition of feedstuffs and feces. J Agric Food Chem. 1988;36:1202–6. https://doi.org/10.1021/jf00084a019 .

Campbell TW. Avian hematology and cytology. 2nd ed. Ames: Iowa State University Press; 1995.

Natt MP, Herrick CA. A new blood diluent for counting the erythrocytes and leukocytes of the chicken. Poult Sci. 1952;31:735–8. https://doi.org/10.3382/ps.0310735 .

Salamano G, Mellia E, Tarantola M, Gennero MS, Doglione L, Schiavone A. Acute phase proteins and heterophil:lymphocyte ratio in laying hens in different housing systems. Vet Rec. 2010;167:749–51. https://doi.org/10.1136/vr.c5349 .

Raspa F, Stoppani N, Perini F, Fiorilla E, Profiti M, Maione S, et al. Multiplex digital expression gene analysis (MuDEGA) of 11 liver poultry genes with NGS approach. Ital J Anim Sci. 2023;22:1–320. https://doi.org/10.1080/1828051X.2023.2210877 .

Blighe K, Rana S, Lewis M. EnhancedVolcano: publication ready volcano plots with enhanced colouring and labeling. R package version 1.16.0.2021. https://github.com/kevinblighe/EnhancedVolcano .

Saleh EA, Watkins SE, Waldroup PW. High-level usage of dried bakery product in broiler diets. J Appl Poultry Res. 1996;5:33–8.

Al-Tulaihan AA, Najib H, Al-Eid SM. The nutritional evaluation of locally produced dried bakery waste (DBW) in the broiler diets. Pakistan J Nutr. 2004;3:294–9. https://doi.org/10.3923/pjn.2004.294.299 .

Potter LM, Shelton JR, Kelly M. Effects of zinc bacitracin, dried bakery product and different fish meals in diets of young turkeys. Poult Sci. 1971;50:1109–15.

Tretola M, Luciano A, Ottoboni M, Baldi A, Pinotti L. Influence of traditional vs alternative dietary carbohydrates sources on the large intestinal microbiota in post-weaning piglets. Animals. 2019;9(8):516. https://doi.org/10.3390/ani9080516 .

Abdollahi MR, Zaefarian F, Ravindran V. Feed intake response of broilers: impact of feed processing. Anim Feed Sci Technol. 2018;237:154–65. https://doi.org/10.1016/j.anifeedsci.2018.01.013 .

Svihus B. Starch digestion capacity of poultry. Poult Sci. 2014;93:2394–9. https://doi.org/10.3382/ps.2014-03905 .

Oketch EO, Wickramasuriya SS, Oh S, Choi JS, Heo JM. Physiology of lipid digestion and absorption in poultry: an updated review on the supplementation of exogenous emulsifiers in broiler diets. J Anim Physiol Anim Nutr (Berl). 2023;107:1429–43. https://doi.org/10.1111/jpn.13859 .

Gross WB, Siegel HS. Evaluation of the heterophil/lymphocyte ratio as a measure of stress in chickens. Avian Dis. 1983;27:972–9.

Fajstova A, Galanova N, Coufal S, Malkova J, Kostovcik M, Cermakova M, et al. Diet rich in simple sugars promotes pro-inflammatory response via gut microbiota alteration and TLR4 signaling. Cells. 2020;9:2701. https://doi.org/10.3390/cells9122701 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Kang DR, Belal SA, Tian W, Park BY, Choe HS, Shim KS. Effect of dietary gluten content on small intestinal inflammatory response of broilers. Eur Poult Sci. 2019;83:285. https://doi.org/10.1399/eps.2019.285 .

Wu TC, Donaldson WE. Effect of cholesterol feeding on serum lipoproteins and atherosclerosis in atherosclerosis-susceptible and atherosclerosis-resistant Japanese quail. Poult Sci. 1982;61:2407–14.

Siegel HS, Hammad SM, Leach RM, Barbato GF, Green MH, Marks HL. Dietary cholesterol and fat saturation effects on plasma esterified and unesterified cholesterol in selected lines of Japanese quail females. Poult Sci. 1995;74:1370–80. https://doi.org/10.3382/ps.0741370 .

Velasco S, Ortiz LT, Alzueta C, Rebolé A, Treviño J, Rodríguez ML. Effect of inulin supplementation and dietary fat source on performance, blood serum metabolites, liver lipids, abdominal fat deposition, and tissue fatty acid composition in broiler chickens. Poult Sci. 2010;89:1651–62. https://doi.org/10.3382/ps.2010-00687 .

Rosell CM, Garzon R. Chemical composition of bakery products. Handbook of Food Chemistry. Springer, Berlin, Heidelberg; 2015. https://doi.org/10.1007/978-3-642-36605-5_22 .

Abdi-Hachesoo B, Talebi A, Asri-Rezaei S. Comparative study on blood profiles of indigenous and Ross-308 broiler breeders. Glob Vet. 2011;7:238–41.

Goel A, Ncho CM, Choi YH. Regulation of gene expression in chickens by heat stress. J Anim Sci Biotechnol. 2021;12:11. https://doi.org/10.1186/s40104-020-00523-5 .

Sauvant D, Perez JM, Tran G, (Eds.). Tables of composition and nutritive value of feed materials: Pigs, poultry, cattle, sheep, goats, rabbits, horse and fish. 2nd ed. Wageningen and Paris: Wageningen Academic Publishers and INRA editions; 2004. p. 301. https://doi.org/10.3920/978-90-8686-668-7 .

Download references

Acknowledgements

The authors would like to express their gratitude to Dalma Mangimi Spa in Cuneo, Italy, for providing cFF ingredient for this experiment. Special thanks are also extended to Mr. Dario Sola for his invaluable assistance in chicken management and feed processing.

The experiment was funded by the Department of Veterinary Sciences “Ricerca Locale – Linea A”.

Author information

Achille Schiavone and Claudio Forte are co-last authors.

Authors and Affiliations

Department of Veterinary Sciences, University of Turin, Grugliasco, Italy

Karthika Srikanthithasan, Marta Gariglio, Elena Diaz Vicuna, Edoardo Fiorilla, Barbara Miniscalco, Valeria Zambotto, Eleonora Erika Cappone, Nadia Stoppani, Dominga Soglia, Federica Raspa, Joana Nery, Andrea Giorgino, Achille Schiavone & Claudio Forte

Animal Nutrition and Welfare Service (SNiBA), Department of Animal and Food Sciences, Universitat Autònoma de Barcelona, Barcelona, Spain

Departamento de Producción Animal, Universidad de Córdoba, Córdoba, Spain

Andrés Luis Martínez Marínz

Department of Animal Production, University of Murcia, Murcia, Spain

Josefa Madrid Sanchez

You can also search for this author in PubMed   Google Scholar

Contributions

KS, AS, CF; Conceptualization. KS, CF; Data Curation. KS, MG, DS, NS; Formal analysis. KS, MG, EDV, EF, BM, VZ, EEC, DS, FR, JN, AG, RS, ALMM, JMS; Investigation. KS, MG, DS, AS, CF; Methodology. KS, CF; Writing—original draft. KS, MG, AS, CF; Writing—review and editing. All authors reviewed and approved the final version of the manuscript.

Corresponding author

Correspondence to Marta Gariglio .

Ethics declarations

Ethics approval and consent to participate.

The experimental protocol was approved by the Bioethical Committee of the Department of Veterinary Sciences, University of Turin, Italy (Protocol no. 245, 01/01/2022).

Consent for publication

Not applicable.

Competing interests

The authors declared there is no conflict of interest.

Supplementary Information

Additional file 1: table s1..

Effect of the different levels of cFF in broiler diets on the liver gene abundance.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Srikanthithasan, K., Gariglio, M., Diaz Vicuna, E. et al. Dietary processed former foodstuffs for broilers: impacts on growth performance, digestibility, hematobiochemical profiles and liver gene abundance. J Animal Sci Biotechnol 15 , 122 (2024). https://doi.org/10.1186/s40104-024-01081-w

Download citation

Received : 06 May 2024

Accepted : 24 July 2024

Published : 08 September 2024

DOI : https://doi.org/10.1186/s40104-024-01081-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Alternative feed
• Broiler chicken
• Former foodstuff
• Gene abundance

Journal of Animal Science and Biotechnology

ISSN: 2049-1891

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]