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• Published: 21 October 2020

A brief guide to good practices in pharmacological experiments: Western blotting

• Han Xiao 2 ,
• Da-lei Wu 3 ,
• Ye Yang 4 &
• Pei Wang 5 , 6  

Acta Pharmacologica Sinica volume  42 ,  pages 1015–1017 ( 2021 ) Cite this article

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Western blotting (WB) is an antibody-based experimental technique used to detect and quantify target proteins, which are often within a complex mixture extracted from cells or tissue. Although there are many new alternative technologies, such as enzyme-linked immunosorbent assay (ELISA), immunofluorescence, and mass spectrometry (MS), they all have their own limitations to some extent. ELISA lacks loading controls, immunofluorescence is an in situ technique and is semiquantitative, while MS is expensive and depends on the experimental technique and conditions. Therefore, WB remains the most commonly used methodology in the lab for protein detection. However, concerns about WB have been voiced by many scientific journals in an effort to reduce potential mistakes and increase reproducibility [ 1 ]. Here, we will focus on some essential caveats during the WB experiment. This guide, therefore, aims to provide an updated and more concise and useable reference for future experiments and paper writing.

WB includes the following steps. First, proteins are separated from the mixture by sodium dodecyl sulfate–polyacrylamide gel electrophoresis according to their molecular weights. Next, the separated proteins are transferred and bound to a solid membrane. Then, the target protein on the membrane is detected by the immunological method. The identification of a specific protein is based on two parameters: molecular weight and signal intensity. Molecular weight could be estimated by prestained molecular weight markers. The signal is determined by a secondary antibody following the addition of primary antibodies to detect the protein blotted onto the membrane.

Since WB involves multiple steps for detection of different proteins, there is no one particular set of optimal conditions suitable for all proteins. Researchers usually spend considerable time optimizing the conditions to obtain the best signal-to-noise ratios, yet difficulties persist in obtaining consistent and high-quality results. Many specific techniques used in the experiment influence the result of WB, among which the experimental controls, the characterization of antibodies, the choice of loading controls, and the image processing and presentation are the most noticeable challenges. Next, we will discuss those important aspects of WB.

Sample preparation will directly affect the quality of the results, so the choice of correct lysis buffer is a critical step. In general, lysis buffers containing nonionic detergents such as NP-40 or Triton X-100 are sufficient to release proteins from cells, while ionic detergents such as SDS and sodium deoxycholate can be considered for harsh extraction conditions. Thus, the most commonly used commercial lysis buffers are radioimmunoprecipitation assay buffer containing SDS and NP-40 buffer without SDS. In special cases, guanidine-HCl, a chaotropic agent, can be added into lysis buffer to denature oligomerized proteins into their native conformations. Moreover, proteolysis could be inhibited by protease inhibitors, such as PMSF, pepstatin, and EDTA; and protein dephosphorylation could be prevented by phosphatase inhibitors, such as NaF and Na 3 VO 4 . Thus, the appropriate commercial protease inhibitor cocktail could be used according to specific needs.

After sample lysis, the protein concentration is measured before the next procedure. Various methods for protein concentration detection can be applied, including the Bradford assay, Lowry assay, and bicinchoninic assay (BCA). The Bradford assay is based on the absorption of the dye Coomassie Blue G-250 by proteins. The principles of the Lowry assay and BCA assay are similar and rely on color development from the Biuret reaction based on the concentrations of the proteins dissolved in samples. The advantages of the Bradford assay are that it is easy and quick to perform with one reagent, while the advantages of the Lowry assay and BCA assay are their extreme sensitivity and improved compatibility with a wide range of detergents (SDS, Triton X-100, Tween 20, etc.).

It is important to set both positive and negative controls for the detected proteins to validate the WB results. Genetically modified animal tissue or cells are suggested as choices for controls. We can verify the correct molecular weight by comparing the wild-type sample with the knockout or knockdown animal or cell sample. Controls lacking the primary antibody or the blocking peptide of the antibody can verify the specificity of the antibody used in WB. Although the above-mentioned controls may not always be available for all proteins, positive and negative controls need to be included as much as possible.

The selectivity of antibodies directly affects WB results, and poor selectivity may lead to the misinterpretation of the results. There are currently databases that can be used for choosing characterized antibodies with high selectivities, such as Antibodypedia ( https://www.antibodypedia.com/ ), the Human Protein Atlas ( http://www.proteinatlas.org/ ), and the Antibody Registry ( https://antibodyregistry.org/ ). For unvalidated antibodies, there are suggested methodologies to validate the selectivity of the antibodies [ 2 ]. These methods include detection of whether the signal is eliminated or significantly reduced after genetic knockout or knockdown of the target gene; analysis of the correlation between WB signals and signals of other detection methods (e.g., MS) in a set of different samples with variable expression of the target protein; analysis of the correlation of protein levels by using two or more independent antibodies targeting different epitopes of the same protein; expression of the target protein with a tag, and analysis of the correlation between antibody labeling and the detection of the tag. If the results are highly correlated, then the antibody is validated for WB analysis.

Usually, there are multiple secondary antibodies suitable for the subsequent detection of the target protein, and the selection can be optimized in specific experiments. When selecting a secondary antibody, both the type of primary antibody and the requirements of subsequent detection schemes should be considered comprehensively:

Species source of the primary antibody: the reactivity of the secondary antibody should be consistent with the species source of the primary antibody used. For example, if the primary antibody is a mouse-derived monoclonal antibody, an anti-mouse secondary antibody (goat anti-mouse or rabbit anti-mouse) should be selected.

Type of primary antibody: the secondary antibody must match the class or subclass of the primary antibody. This is usually applicable for monoclonal antibodies. Polyclonal antibodies are mainly IgG immunoglobulins, so the corresponding secondary antibodies are anti-IgG antibodies. If the primary antibody is mouse IgM, then the corresponding secondary antibody should be anti-mouse IgM. If the primary monoclonal antibody is of a certain subclass of mouse IgG (IgG1, IgG2a, IgG2b, or IgG3), then almost all anti-mouse IgG can bind to it, or the secondary antibody can be selected to specifically target this subclass. If the type of the primary antibody is not clear, IgG against the corresponding species can be used.

Species source of the secondary antibody: there is usually no predictable connection between species source and the quality of the secondary antibody. However, the use of secondary antibodies from the same species as the primary antibodies should be avoided, especially in double-labeling experiments. If one of the primary antibodies is derived from goat, whereas the other is derived from mice, the corresponding secondary antibodies must be anti-goat and anti-mouse secondary antibodies, respectively. The secondary antibody cannot be derived from goat or mice.

Coupling of probes to the secondary antibody: probes coupled to secondary antibodies mainly include enzymes (such as horseradish peroxidase and alkaline phosphatase), fluorescent molecules (FITC, rhodamine, Texas Red, PE, Dylight, etc.), biotin, and gold particles. The probes can be selected according to the detection system used for WB. For WB and ELISA, the most commonly used secondary antibody is an enzyme-labeled secondary antibody, while cell or tissue labeling experiments (cellular immunochemistry, histoimmunochemistry, and flow cytometry) usually use fluorescent molecule-labeled secondary antibodies.

Another critical issue is the selection of the loading control, which has been widely used in the normalization of WB results to adjust for systematic differences between samples or even between experiments. Housekeeping proteins, such as β-actin and GAPDH, have been commonly used as loading controls. However, the expression of these proteins can change under certain conditions [ 3 , 4 ]. The selected housekeeping proteins need to be proven stable under the experimental conditions. An alternative to the use of a specific protein as the loading control is the staining of total protein. Some methods used for staining of total protein on the membrane, such as Ponceau S [ 5 ] and Fast Green [ 6 ], have been found to be reliable as loading controls. Ponceau S is the most commonly used removable stain and can be conveniently used before immunodetection, but it is relatively insensitive. Fast Green is a more permanent dye used for staining in histology and electrophoresis. It cannot be easily removed and may inhibit subsequent immunodetection. Alternatively, staining with Fast Green after immunodetection has been used in some recent publications. Moreover, some housekeeping proteins are also used as markers for subcellular compartments according to their intracellular distribution [ 7 ] (Table  1 ).

Therefore, to achieve reproducible WB results, the following information should be provided in “Materials and Methods” of a paper:

The primary antibody species (for monoclonal or polyclonal antibodies), isotype (IgG, IgY, etc.), and epitopes generated.

Secondary antibody species, isotype, and labeling.

Source of the primary and secondary antibodies; catalog and lot numbers are needed if they were obtained from a commercial company.

Dilution and incubation conditions of the primary and secondary antibodies.

Type of blotting membrane (nitrocellulose, polyvinylidene fluoride, etc.).

Blocking agents (bovine serum albumin (0.2%–5.0%), nonfat milk, casein, gelatin, etc.).

The most critical rule for image processing and presentation is to maximally preserve the integrity of the original immunoblots. Full scans or images of uncropped blots should be provided (as supplementary files) to reviewers and editors during the submission of papers. If precut blots are used for the antibody treatment, this must be clearly stated and justified by the authors in the Methods section or figure legends. In addition, the number of repetitions performed for the same WB experiment (usually more than two) should also be stated, especially when representative images from only one experiment are shown.

Oversaturated exposure of blots should be avoided to maintain the band signal intensity (expressed either as optical density or fluorescence units) in the linear range for quantitation. Trial experiments aiming to generate a standard curve are recommended, especially when new antibodies or methods are employed. Fig.  1a shows an example of oversaturated bands, which may have masked or at least reduced the differences among samples.

a Oversaturated bands in the WB panel. b The space (red dotted line) indicates blot splicing. c The cut blot. d The full blot.

Comparisons (whether statistical or not) between bands and normalization to loading controls should only be conducted on the same blot. In case the number of samples exceeds the capacity of one single gel, the same control sample in the exact same amount can be included on separate gels. However, comparison with this control sample should still be limited to samples within the same blot.

Separate blots should never be merged into one image. If multiple blots are organized side by side in one figure panel, there should be clearly visible space between them (as shown in Fig.  1b ). If certain lanes contain data not relevant to the topic, they can be cut out from the blot, but the full blot should still be provided to editors or reviewers according to the journals’ requirements (Fig.  1c, d ). However, if these irrelevant lanes were located in the middle of a blot, they should not be simply removed. In this case, the gel should be rerun with reorganized samples. The positions of molecular weight markers should be shown or marked on all the blot images. If the blots have been cropped horizontally, at least two neighboring marker positions (i.e., above and below the bands) should be indicated, as shown in Fig.  1c .

Any image adjustments (e.g., brightness, contrast, rotation, and resizing) should be applied to the whole blot (not just a certain portion of it) to ensure that no specific feature of the original data is eliminated or misrepresented. Figures in TIFF format are preferred. For WB images, a minimum resolution of 300 dpi is required.

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Department of Pharmacology, School of Basic Medical Sciences, Peking University and Beijing Key Laboratory of Tumor Systems Biology, Peking University, Beijing, 100191, China

Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Beijing Key Laboratory of Cardiovascular Receptors Research, Beijing, 100191, China

Helmholtz International Lab, State Key Laboratory of Microbial Technology, Shandong University, Qingdao, 266237, China

School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China

Department of Pharmacology, Second Military Medical University, Shanghai, 200433, China

Department of Pharmacy, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200072, China

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Tie, L., Xiao, H., Wu, Dl. et al. A brief guide to good practices in pharmacological experiments: Western blotting. Acta Pharmacol Sin 42 , 1015–1017 (2021). https://doi.org/10.1038/s41401-020-00539-7

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Received : 26 May 2020

Accepted : 14 September 2020

Published : 21 October 2020

Issue Date : July 2021

DOI : https://doi.org/10.1038/s41401-020-00539-7

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Explore the World of Pharmacology | With+15 Experiments Introduced by PraxiLabs

Last Updated on June 28, 2023 by Nourhan Essam

Pharmacology plays a crucial and vital role in modern medicine. From ancient remedies to modern drugs, pharmacology has evolved significantly over the centuries. It studies how drugs interact with different living organisms and how they can be used to treat and prevent diseases for a healthier life. In this article, we will take a closer look at the science of pharmacology, its definition, branches, history, and importance. We will also show yo u some examples of pharmacology experiments that are introduced by PraxiLabs virtual labs.

Table of Contents

Pharmacology Definition

In simple words, pharmacology is the science of drugs. Also, we can define pharmacology as the science that studies the chemistry, origin, effects, properties, mechanisms, toxicity, and different uses of drugs. It is the study of chemicals and living organisms’ interactions and the effect of chemicals on the biochemical function whether it is normal or abnormal.

 What is Pharmacology?

Pharmacology is considered as a branch of biology and medicine that deals with drug action.

Note that a drug is any molecule (natural, artificial, or endogenous from within the body) that exerts physiological or biochemical effects on the cells, tissues, organs, or organisms.

 Types of Pharmacology

There are 2 main Branches of Pharmacology:

Pharmacokinetics

“Pharmacokinetics” word is derived from 2 words, Pharmacon which means drug and kinetics which means putting in motion. Pharmacokinetics refers to what our bodies do to the drugs we consumed like distribution, absorption, metabolism and extraction processes.

Pharmacodynamics

Pharmacodynamics is the branch of Pharmacology that deals with the drugs mechanism of action. It studies the effects of a drug on biological systems ex: our bodies, microorganisms, and parasites . Pharmacodynamics refers to what drugs do to the body.

Other types of pharmacology are:

Clinical Pharmacology

Clinical pharmacology is the scientific study of medication in man. It includes mainly pharmacodynamics and pharmacokinetic investigations in both healthy and diseased humans. It also deals with detecting the effective, safe, and economical use of drugs in patients.

The main objectives of clinical pharmacology are minimizing the adverse effects of medications and maximizing the effect of drugs, ensuring and promoting the safety of prescriptions.

The other branches of pharmacology are Therapeutics/ Toxicology / Pharmacogenetics/ Pharmacy/Chemotherapy/ Clinical Pharmacology / Pharmacognosy/ Pharmacoeconomics/ Pharmacoepidemiology/ Animal Pharmacology/ Pharmacoeconomics/ Posology/ Comparative Pharmacology.

With PraxiLabs’ pharmacology virtual labs , students can practice the same experiment an unlimited number of times, with 100%supervision & 0% risk! Save your time and money and create your free account!

History of Pharmacology

The following video describes the history of pharmacology

The Importance of Pharmacology

Pharmacology helps people to improve their life and live healthier for longer. We can find the importance of pharmacology everywhere. It has a vital role in:

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Understand how to determine apoptotic cells by visualizing the caspase 3 enzyme activity inside cellular nuclei using a fluorescent microscope (caspase-3 activity in apoptotic cells). Students will be able to treat the cells with the caspase 3 primary and secondary antibodies, also analyze cells and the resultant data. 

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In Vitro Acid Phosphatase Assay for Cell Viability Virtual Lab Simulation

Understand how to quantify the acid phosphatase activity amount on the cell membrane of viable cells by using a microplate reader.

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Drug testing.

Shawn E. McNeil ; Richard J. Chen ; Mark Cogburn .

Affiliations

Last Update: July 29, 2023 .

• Continuing Education Activity

Drug testing detects the presence or absence of a drug or its metabolites in a biological sample. This process can be completed in a variety of settings and with a variety of techniques. Despite the drawbacks, drug testing plays an essential role in the clinical setting because clinical examination, patient self-reporting, and collateral reporting will often underestimate the actual incidence of substance use. However, drug testing should always be used with history/physical and psychosocial assessment. This activity describes the process of drug testing, the nuances of drug testing, the interpretation of false positive and false negative results, and the role of the interprofessional team in managing patients who use illicit drugs.

• Identify the function of drug testing.
• Outline the types of biological matrices that can be used for drug testing.
• Describe the issues of concern in regard to drug testing, including issues of false positives and negatives.
• Summarize interprofessional team strategies for improving care coordination and communication to advance the management of patients who abuse drugs and improve outcomes.
• Introduction

Broadly defined, drug testing uses a biological sample to detect the presence or absence of a drug or its metabolites. This process can be completed in a variety of settings and with a variety of techniques. Many drug screening immunoassays were initially designed for use in the workplace as a drug screening tool for employees. As these tests have become cheaper, more readily available, and easier to use, these tests are now standard in many clinical laboratories. Despite their prevalence, many physicians and providers do not understand how these tests function and their associated limitations. [1] [2]  Despite the drawbacks, drug testing plays an essential role in the clinical setting because clinical examination, patient self-reporting, and collateral reporting will often underestimate the actual incidence of substance use. The use of drug screens is also becoming increasingly important in the management of patients with chronic pain and in the treatment of substance use disorders. [3]  

The most commonly tested-for substances are amphetamines, cannabinoids, cocaine, opiates, and phencyclidine (PCP). These drugs are also referred to as the "NIDA five" as these were the five drugs that were recommended for drug screening of federal employees by the National Institute on Drug Abuse (NIDA). This responsibility now falls on the Substance Abuse and Mental Health Services Administration (SAMHSA). There are now expanded drug screens that include testing for oxycodone, methadone, buprenorphine, and fentanyl, among many other drugs.  [4]

There are several biological samples that can be used for testing. These include blood or serum, sweat, hair, oral fluid, nails, and urine. The most commonly used biological sample is urine, as it is non-invasive, and the concentration of a given xenobiotic is generally higher when compared to other samples. This usually results in a higher sensitivity. [3]  Additional considerations include how long a xenobiotic remains detectable in various matrices. It is important to consider these aspects in the context of why testing is being performed.

Immunoassays remain the most common and easily accessible form of testing. More advanced methods, particularly in confirmatory testing, are available and include gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS). These advanced methods tend to have higher specificity and sensitivity as compared to immunoassays, but are more expensive and require specialized equipment and training. [5]

Although drug testing can be used to confirm the recent use of a substance, it also has a role in the diagnosis, treatment, and monitoring of addiction. As a tool for monitoring, this type of testing has the potential to measure the performance of a patient’s substance use treatment. Urine drug testing (UDT) remains the most common modality for detecting drugs in the human body, and it is based on immunoassay techniques. Clinicians should understand the value of random drug testing as opposed to scheduled testing, particularly in the context of a suspected or established substance use disorder. [6] [7]

There are several factors that need to be considered when choosing a particular drug test. Using a certain matrix or rotating matrices can reduce the chances of sample tampering. There are also times when a certain matrix may not be appropriate (i.e., using a hair sample with chemically treated hair or using directly observed urine testing when a patient has had sexual trauma). There is also often a tradeoff when choosing one test over another. Some tests may be regarded as “presumptive” while others are regarded as “definitive." Presumptive tests will typically give a faster result, which may aid in timely clinical decision-making. However, the specificity and/or sensitivity may be lower compared to a definitive test. There may be times when the patient will dispute the results of a presumptive test, and a more definitive test can then be done to provide clarification. A clinician may consider going directly to definitive testing if there is no reasonable presumptive test or if the results of the testing will have major implications.

An important consideration in drug testing is establishing a predetermined cutoff concentration. This value determines the concentration of a xenobiotic in a specimen that results in a positive test. This threshold should be sufficiently high to prevent false-positive results due to cross-reactivity and variability in the test itself and be low enough to prevent false negatives in those who regularly use the xenobiotic being tested for. The exact cutoff can vary depending on the specific xenobiotic, the immunoassay being used, or the clinical context. [3] [8]

Types of Matrices

Urine testing typically has a detection window of hours to days. It usually takes two hours before a substance can be detected in the urine. Factors like urine pH and fluid intake can have an impact on the results. Urine is the most well-established matrix and is the most commonly used matrix for point-of-care testing (POCT). The detection window can be up to 4 days for some substances. Chronic use can extend this window out to weeks. [9] Although sample tampering may be an issue, clinicians have the option of having observed sample collection in many cases. However, this may not completely remove the possibility of sample tampering. Substances that are likely to be tested for with a urine sample include alcohol, amphetamines, benzodiazepines, opiates/opioids, cocaine, and cannabis.

Blood testing is primarily done in emergency situations and is typically used to detect ethanol levels. The advantage of blood testing is that it allows for a precise level to be assessed. Additionally, testing for drugs of abuse often requires that samples be sent to an outside lab. The detection window is usually one to two days. [9] The downside includes the invasiveness of the test, the need for someone skilled to obtain the sample, and the fact that the sample can be a potential biohazard. 

Breath testing is also primarily used for alcohol detection. More precisely, it allows for the assessment of recent alcohol use. The result is called a breath alcohol concentration or BrAC. The BrAC is often used as an estimate of blood alcohol concentration (BAC). However, on an individual basis, the BrAC can either overestimate or underestimate the blood ethanol level. Research has been focusing on the potential use of breath testing for the detection of cocaine, cannabis, benzodiazepines, amphetamines, opioids, methadone, and buprenorphine. [10] [11]

Oral fluid testing (OFT) generally detects concentration that correlates with plasma concentrations. However, the concentration of orally consumed substances will be higher. Oral testing is more likely to detect parent compounds versus urine testing which relies more heavily on metabolite concentrations. The detection window varies but can be up to 48 hours. [9]

Sweat testing is completed through the use of an absorbent pad that is collected. The results of testing this pad give a cumulative concentration that suggests how much of a substance that person consumed over the entire period that the pad was worn. There can be problems with contamination or incomplete adhesion of the patch to the skin. One benefit of sweat testing is that it gives a detection window of hours to weeks.

Hair, as a matrix for the detection of substances, can provide information on cumulative substance use. Similar to sweat testing, hair testing has a long detection window. Scalp hair has a detection window of three months, while slower-growing body hair (such as pubic or axillary hair) has a detection window of up to 12 months. The results of the hair testing can vary based on the individual in regards to the characteristics of their hair. Hair testing can be used for the detection of cocaine, phencyclidine (PCP), amphetamines, opioids, and 3,4-Methylenedioxymethamphetamine (MDMA). Potential shortcomings of this matrix include price, environmental cross-contamination, and issues during decontamination prior to testing and establishing cut-off values. [12]

Often, contingency management is paired with drug testing in the treatment of addiction, and behavioral incentives are allowed on the basis of a negative result. These incentives (or “reinforcers”) can be vouchers or prizes that are given to encourage the patient to continue their abstinence from a particular substance. The use of POC testing, in particular, makes the implementation of contingency management easier because of the rapid results that can be achieved. [13] [14]

• Issues of Concern

The technologies behind drug testing as well as the clinical application of this technology are rapidly evolving. It is important that clinicians understand the proper use of current methods and stay informed about emerging techniques. One of the biggest challenges facing clinicians is the accurate detection of drugs that have relevance to clinical outcomes. [15]  

As with all testing, false negatives and false positives are a possibility. False negatives are defined as a negative test result despite the presence of a given xenobiotic. A false positive is defined as a positive test result despite no xenobiotic present in a sample. Common false positives are described in the testing of amphetamines and include medications such as selegiline, bupropion, and pseudoephedrine. [16]

False negatives are well described when testing for opioids and benzodiazepines. The opiate screen on the UDT specifically detects morphine. As a result, this screen would not detect synthetic opioids, such as fentanyl and methadone, or other opioids that are structurally dissimilar such as buprenorphine, oxycodone, and hydrocodone. Similarly, the benzodiazepine screen tests for a metabolite, oxazepam. It would be expected to test negative for benzodiazepines such as lorazepam, clonazepam, and alprazolam as these are not metabolized to oxazepam.

Depending on the xenobiotic being tested for, there can be multiple other xenobiotics that can crossreact and cause a false positive result. These are commonly described in testing for amphetamines, PCP, cannabinoids, and methadone. [3] [17] [18]  It is important to consider what medications patients may be taking when a positive result is obtained on presumptive testing. Common over-the-counter medications, such as diphenhydramine and dextromethorphan can result in a positive result on the PCP screen.

The protocols in place when performing drug testing in the workplace vs a clinical setting vary in several important aspects. Confirmatory testing is not standard procedure in the clinical setting, as the results from a presumptive test are often enough to direct clinical decisions. In the workplace, when a presumptive test is positive, it is routinely sent off for confirmatory testing so that an appropriate decision can be made concerning employment. By extension, in workplace testing, the chain of custody is important to maintain the validity of testing. It is not uncommon for results to be disputed, and so maintenance of the chain of custody will make it more difficult to invalidate results due to a procedural problem. [8]

A major concern in testing, particularly in UDT is the possibility of sample tampering. One common method is dilution, either by dilution by adding an adulterant or by increasing fluid intake prior to the test. Common adulterants include household items, such as bleach, laundry detergent, and table salt. Commercial products directed at bypassing UDT also exist, such as UrinAid (glutaraldehyde), Stealth (containing peroxidase and peroxide), Urine Luck (pyridinium chlorochromate), and Klear (potassium nitrite). These products are easily obtained through various internet sources. Synthetic urine is another common adulterant. These adulterants can be used to bypass both presumptive and confirmatory tests. For example, glutaraldehyde is commonly used to create a false negative for cannabinoids. Oxidative additives can react with drug and drug metabolites and make them undetectable with common immunoassays. Fortunately, there have been advancements in laboratory testing to detect common adulterants. [19]

The current drug landscape is continually changing. New synthetic drugs continue to be made and enter the current drug supply. Many of these drugs are structurally unlike any current existing drugs and as a result, are not detectable with current testing. This can make it difficult to determine if someone is using a particular substance. Continued research and development are needed to keep pace with new synthetic drugs and to address continued attempts at test adulteration. [4] [20]

• Clinical Significance

Drug testing can be used to further assess a patient presenting for evaluation or be used in the workplace for employment eligibility.

A positive result on a drug test tells the clinician that the patient had a detectable amount of a substance present during a certain window of time. This result does not typically indicate that impairment is the result of any particular substance or that the patient has a substance use disorder. When considering a positive result, confirmatory testing may be helpful in verifying results in certain situations. As discussed previously, false positives can occur due to cross-reactivity between other substances not being tested for and the immunoassay being used.

A major consideration when using drug testing is regarding the significance of a negative result. Clinicians should bear in mind that a negative result simply means that the particular substance being tested for was not detected. [21] This may mean its level was not sufficient enough to be detected or that use of that substance did not occur during the detection window. A negative result does not rule out the use of a substance or the presence of a substance use disorder. False negatives are not uncommon, particularly if the clinician is not aware of what is being tested for in a given immunoassay. A common example is in the testing of benzodiazepines, where the immunoassay is directed at the detection of oxazepam and is not intended to detect benzodiazepines such as clonazepam or alprazolam. 

It is essential that clinicians understand the testing methodology of various drug testing modalities as well as their associated sensitivity, specificity, and significance of false-negative and false-positive results. 

• Other Issues

Another issue regarding the use of drug testing in the clinical setting is the attitude of some patients that the results of a drug test may be used in a punitive way. It is important that clinicians are forthcoming about how drug testing is being used. Specifically, in the clinical setting, clinicians should make it clear that a drug test will not be used in a punitive fashion and is being used to help improve the care that the patient is receiving.

Another area of concern is the issue of drug testing adolescents. In particular, the American Academy of Pediatrics Committee on Substance Abuse has made it clear that involuntary drug testing of an adolescent with decisional capacity is inappropriate. [18]

• Enhancing Healthcare Team Outcomes

Drug testing is now commonly done in clinical medicine for a variety of reasons. Because of the stigma associated with drug use and positive drug tests, the physician has an important role in setting the non-judgemental tone that may influence a patient's care. It is important that every member of the care team (physicians, nursing, ancillary staff, etc.) understand why drug testing is being performed. In the clinical setting, positive drug test results should not be used for punitive purposes. Instead, the result should be looked at as an opportunity to have a discussion concerning potential drug use in a patient. Finally, random drug testing on every patient is not recommended; it has to be supported by history and a physical exam. Healthcare workers including nurses and pharmacists should be aware of the laws surrounding drug usage, drug test results, and confidentiality laws. [15]

Drug testing in the workplace is vastly different in purpose than in the clinical setting and it is an important tool to identify those who may be working under the influence, as this can be a potential safety concern. It is important that test results are confirmed prior to any sanctions against an employee being made.

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Disclosure: Shawn McNeil declares no relevant financial relationships with ineligible companies.

Disclosure: Richard Chen declares no relevant financial relationships with ineligible companies.

Disclosure: Mark Cogburn declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page McNeil SE, Chen RJ, Cogburn M. Drug Testing. [Updated 2023 Jul 29]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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• Review Biological testing for drugs of abuse. [EXS. 2010] Review Biological testing for drugs of abuse. Vearrier D, Curtis JA, Greenberg MI. EXS. 2010; 100:489-517.
• Review Drug testing in the era of new psychoactive substances. [Adv Clin Chem. 2022] Review Drug testing in the era of new psychoactive substances. Gerona RR, French D. Adv Clin Chem. 2022; 111:217-263. Epub 2022 Sep 23.
• Rapid Extraction and Qualitative Screening of 30 Drugs in Oral Fluid at Concentrations Recommended for the Investigation of DUID Cases. [J Anal Toxicol. 2022] Rapid Extraction and Qualitative Screening of 30 Drugs in Oral Fluid at Concentrations Recommended for the Investigation of DUID Cases. Coulter C, Garnier M, Moore C. J Anal Toxicol. 2022 Oct 14; 46(8):899-904.
• Workplace drug testing in Italy: findings about second-stage testing. [Drug Test Anal. 2015] Workplace drug testing in Italy: findings about second-stage testing. Vignali C, Stramesi C, Morini L, San Bartolomeo P, Groppi A. Drug Test Anal. 2015 Mar; 7(3):173-7. Epub 2014 Mar 20.
• Oral fluid testing for drugs of abuse: positive prevalence rates by Intercept immunoassay screening and GC-MS-MS confirmation and suggested cutoff concentrations. [J Anal Toxicol. 2002] Oral fluid testing for drugs of abuse: positive prevalence rates by Intercept immunoassay screening and GC-MS-MS confirmation and suggested cutoff concentrations. Cone EJ, Presley L, Lehrer M, Seiter W, Smith M, Kardos KW, Fritch D, Salamone S, Niedbala RS. J Anal Toxicol. 2002 Nov-Dec; 26(8):541-6.

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Experiment Definition in Science – What Is a Science Experiment?

In science, an experiment is simply a test of a hypothesis in the scientific method . It is a controlled examination of cause and effect. Here is a look at what a science experiment is (and is not), the key factors in an experiment, examples, and types of experiments.

Experiment Definition in Science

By definition, an experiment is a procedure that tests a hypothesis. A hypothesis, in turn, is a prediction of cause and effect or the predicted outcome of changing one factor of a situation. Both the hypothesis and experiment are components of the scientific method. The steps of the scientific method are:

• Make observations.
• Ask a question or identify a problem.
• State a hypothesis.
• Perform an experiment that tests the hypothesis.
• Based on the results of the experiment, either accept or reject the hypothesis.
• Draw conclusions and report the outcome of the experiment.

Key Parts of an Experiment

The two key parts of an experiment are the independent and dependent variables. The independent variable is the one factor that you control or change in an experiment. The dependent variable is the factor that you measure that responds to the independent variable. An experiment often includes other types of variables , but at its heart, it’s all about the relationship between the independent and dependent variable.

Examples of Experiments

Fertilizer and plant size.

For example, you think a certain fertilizer helps plants grow better. You’ve watched your plants grow and they seem to do better when they have the fertilizer compared to when they don’t. But, observations are only the beginning of science. So, you state a hypothesis: Adding fertilizer increases plant size. Note, you could have stated the hypothesis in different ways. Maybe you think the fertilizer increases plant mass or fruit production, for example. However you state the hypothesis, it includes both the independent and dependent variables. In this case, the independent variable is the presence or absence of fertilizer. The dependent variable is the response to the independent variable, which is the size of the plants.

Now that you have a hypothesis, the next step is designing an experiment that tests it. Experimental design is very important because the way you conduct an experiment influences its outcome. For example, if you use too small of an amount of fertilizer you may see no effect from the treatment. Or, if you dump an entire container of fertilizer on a plant you could kill it! So, recording the steps of the experiment help you judge the outcome of the experiment and aid others who come after you and examine your work. Other factors that might influence your results might include the species of plant and duration of the treatment. Record any conditions that might affect the outcome. Ideally, you want the only difference between your two groups of plants to be whether or not they receive fertilizer. Then, measure the height of the plants and see if there is a difference between the two groups.

Salt and Cookies

You don’t need a lab for an experiment. For example, consider a baking experiment. Let’s say you like the flavor of salt in your cookies, but you’re pretty sure the batch you made using extra salt fell a bit flat. If you double the amount of salt in a recipe, will it affect their size? Here, the independent variable is the amount of salt in the recipe and the dependent variable is cookie size.

Test this hypothesis with an experiment. Bake cookies using the normal recipe (your control group ) and bake some using twice the salt (the experimental group). Make sure it’s the exact same recipe. Bake the cookies at the same temperature and for the same time. Only change the amount of salt in the recipe. Then measure the height or diameter of the cookies and decide whether to accept or reject the hypothesis.

Examples of Things That Are Not Experiments

Based on the examples of experiments, you should see what is not an experiment:

• Making observations does not constitute an experiment. Initial observations often lead to an experiment, but are not a substitute for one.
• Making a model is not an experiment.
• Neither is making a poster.
• Just trying something to see what happens is not an experiment. You need a hypothesis or prediction about the outcome.
• Changing a lot of things at once isn’t an experiment. You only have one independent and one dependent variable. However, in an experiment, you might suspect the independent variable has an effect on a separate. So, you design a new experiment to test this.

Types of Experiments

There are three main types of experiments: controlled experiments, natural experiments, and field experiments,

• Controlled experiment : A controlled experiment compares two groups of samples that differ only in independent variable. For example, a drug trial compares the effect of a group taking a placebo (control group) against those getting the drug (the treatment group). Experiments in a lab or home generally are controlled experiments
• Natural experiment : Another name for a natural experiment is a quasi-experiment. In this type of experiment, the researcher does not directly control the independent variable, plus there may be other variables at play. Here, the goal is establishing a correlation between the independent and dependent variable. For example, in the formation of new elements a scientist hypothesizes that a certain collision between particles creates a new atom. But, other outcomes may be possible. Or, perhaps only decay products are observed that indicate the element, and not the new atom itself. Many fields of science rely on natural experiments, since controlled experiments aren’t always possible.
• Field experiment : While a controlled experiments takes place in a lab or other controlled setting, a field experiment occurs in a natural setting. Some phenomena cannot be readily studied in a lab or else the setting exerts an influence that affects the results. So, a field experiment may have higher validity. However, since the setting is not controlled, it is also subject to external factors and potential contamination. For example, if you study whether a certain plumage color affects bird mate selection, a field experiment in a natural environment eliminates the stressors of an artificial environment. Yet, other factors that could be controlled in a lab may influence results. For example, nutrition and health are controlled in a lab, but not in the field.
• Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
• 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.
• Holland, Paul W. (December 1986). “Statistics and Causal Inference”.  Journal of the American Statistical Association . 81 (396): 945–960. doi: 10.2307/2289064
• Stohr-Hunt, Patricia (1996). “An Analysis of Frequency of Hands-on Experience and Science Achievement”. Journal of Research in Science Teaching . 33 (1): 101–109. doi: 10.1002/(SICI)1098-2736(199601)33:1<101::AID-TEA6>3.0.CO;2-Z

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3 Drug Analysis

Introduction.

Identify unknown drugs in powder and pill form using presumptive and confirmatory techniques.

Drug Identification

Analysis of controlled and uncontrolled substances is often fairly routine. After a visual assessment and measuring the mass of the exhibit, we will typically perform presumptive testing then use a different technique to confirm the identity of an unknown. While some labs or specialists may need to quantitatively characterize the various components of an unknown drug, we’ll focus on simple identification since that is the most routine task required.

Explanations and examples of presumptive and confirmatory testing are provided below. Regardless of the specific methods used in our analytical scheme, both analyses must yield results that agree with one another in order to confirm the identity of an unknown substance. There are some possible exceptions to this sequence of events, though. One example of an exception is non-controlled pharmaceutical preparations, which is also explained below.

Presumptive Testing

Presumptive testing is a quick, cheap, useful tool that can help inform future analysis and often serves as 1/2 of the concurrent positives required in most drug identifications. A common method to perform this testing is by using one or more reagents that are expected to change color when exposed to certain functional groups. These color test reagents are usually quite simple and vary in reactivity – they may only change one specific color in response to one specific type of drug or they may exhibit a range of colors in response to a wide variety of drugs.

The Marquis reagent is an example of the latter. A simple formulation of sulfuric acid and formaldehyde technically made for detection of alkaloids, the Marquis reagent may turn shades of orange, brown, red, green, or yellow in the presence of a variety of amphetamines, opiates, and other drugs. While lacking in specificity, the results can often be used in conjunction with other tests or with confirmatory results to reinforce identification of these substances.

Controls should always be used with color tests. A known positive should be used to generate the expected reaction and ensure no false negatives (positive control) . A known negative, or blank, should be used to ensure a reaction does not occur when the target is not present and ensure no false positives (negative control) .

Confirmatory Testing

Confirmatory testing should then typically be performed on presumptive positives. Because confirmatory testing requires expensive instrumentation and personnel, it is helpful to have some direction to save time and money (also – we need two positives most of the time anyways, so confirmatory testing may not be useful without another positive). The gold standard for confirmatory drug testing is usually gas chromatography-mass spectrometry (GC-MS), or, as an alternative, Fourier transform infrared spectroscopy (FTIR).

GC-MS analysis will generate a chromatogram (signal vs. time) and a mass spectrum (signal vs. mass-to-charge ratio, m/z ). The chromatogram will show us the retention time (RT) - how long each compound takes to elute , or travel through the column. The mass spectrum will show us the masses of compound fragments found at each retention time. Together, this information makes GC-MS a very powerful discriminatory tool.

Sample Preparation

Sample preparation can vary depending on the physical form of the drug. For normal GC-MS testing, we need our drug to be dissolved in a volatile organic solvent. So, if the drug is an acidic salt, we need to do a basic liquid-liquid extraction (bicarbonate solution + chloroform, e.g.) to analyze the free-base version in the organic layer via GC-MS. Some labs will perform this extraction on most powders that come in – it saves time when we don’t know what it is, and drugs that are already soluble in the organic layer will still be there. Otherwise, presumptive tests or FTIR analysis can help inform the preparation process. Liquids, plants, and other forms of drugs have other sample prep considerations but we’ll only be looking at powders and pills in this class.

Prior to analysis of unknowns, a control sample must be analyzed using the same instrument method you intend to use on your unknown. This control sample is a mixture containing standards of some or all of the drugs for which that assay is designed and an internal standard (IS) . A blank should also be run to ensure there are no false positives resulting from instrumental/method issues or analyte carryover from a previous run.

The internal standard is a compound that can also be added to our unknown mixture and is ideally structurally similar to our analytes. Internal standards can serve a couple of purposes. First, they are used to confirm the instrument and method are working appropriately for that run – this way, if no other compounds appear in our unknown data, we know the drugs we’re testing for are not present. Second, an internal standard can be used to obtain relative retention times (RT of drugs relative to that of IS to account for acceptable variations).

For a GC-MS analysis to be confirmatory of a drug identification, there are some parameters that must be met:

• If this is not the case, the entire run is generally void
• RT of unknown must be within a given tolerance of the control RT
• Number of peaks required can vary from drug to drug
• Relative intensity of each m/z should also be within a given tolerance compared to control
• No major unidentifiable m/z peaks

Legally Manufactured Pills

Sometimes, legally manufactured, non-controlled drugs are recovered by authorities. Whether or not they were legally possessed, we still often need to identify them. If there are tablets with a pharmaceutical identifier on them, we can often consult reference materials for a presumptive identification and then confirm with FTIR or GC-MS. If you have access to an attenuated total reflectance (ATR) FTIR, you can confirm by placing a tablet directly on the ATR crystal, though grinding into a powder will usually yield a more intense signal. Capsules that can be broken open always need to be emptied prior to confirmatory testing.

If the FTIR results do not support the ID from the pharmaceutical identifiers, GC-MS analysis should generally be performed to confirm the FTIR results.

Materials and Methods

Supplies (part 1).

• internet access
• camera (phone is acceptable)
• FTIR or ATR-FTIR

Procedure (Part 1)

Pills – visual identification.

RxList maintains a free pill lookup resource that allows you to enter markings, shape, and color of pills. If it is a legally manufactured pill, it’s pretty reliable for returning accurate results. The DEA maintains its own Logo Index with over 30000 pills. There is also now software labs can buy to enter photos of pills and automate the identification process.

• Take a close-up photo of any pill evidence you’ve been given
• Enter what information you have into the identifier at https://www.rxlist.com/pill-identification-tool/article.htm

Pills – Analytical Confirmation

To confirm the identity of the pills, we can use FTIR and compare to known results for the same pills.

• Obtain a blank spectrum
• Then, ground the tablet and analyze again to compare
• For capsules, break open and analyze the contents
• Compare to reference spectra
• Take note of parameters and obtain CSVs of your data for your report
• If results do not confirm your ID from the pharmaceutical identifiers, you’ll perform an extraction later to analyze via GC-MS

Supplies (Part 2)

• Components for Marquis Reagent
• Storage bottle
• graduated cylinder
• Testing tray
• Disposable pipette

Procedure (Part 2)

Make your own marquis reagent.

Marquis Reagent is made with concentrated (9598%) sulfuric acid and 40% formaldehyde. You’ll use it to perform colorimetric tests on a variety of samples provided.

• Add 1 mL of formaldehyde to your storage bottle
• Carefully add 20 mL sulfuric acid to the formaldehyde
• You may adjust the total volume with the same ratio if needed (5 mL + 100 mL, e.g.)

Use Your Marquis Reagent

• Place a small amount of each sample into individual wells of your sample tray
• Label each sample with a marker and note your labels in your notebook
• Carefully add a few drops of Marquis Reagent to each well
• Color changes should happen immediately
• Consult a color reference chart like that provided in class to try to presumptively identify each unknown

Supplies (Part 3)

• Control sample with standard mix (TA provided)
• GC sample vials
• Pills from Part 1, if additional analysis needed
• Internal standard solution (details indicated on board)
• Suitable acids may include: hydrochloric (concentrated or diluted) and acetic (concentrated or diluted)
• Suitable bases may include: sodium hydroxide, sodium bicarbonate, and ammonium hydroxide
• Suitable organic solvents may include: acetone, ethyl ether, chloroform, heptane, hexane, methanol, methylene chloride, isopropanol
• Test tubes or other vessels suitable for liquid-liquid extraction

Procedure (Part 3)

In order to analyze your unknown drugs via GC-MS, you’ll first need to get them into a volatile organic solvent. The type of sample (physical form, acid/base properties, etc.) and your instrumental methodology will determine how to best go about doing this. There are a variety of methods including dry solvent extractions, solvent washing, reconstitution, etc., but we will perform only simple acid/base extractions in this exercise.

General Acid/Base and Solubility Reminders

• We need the unionizedform of our drugs, because unionized drugs tend to easily dissolve non-polar organic solvent.
• pKa values can tell us the pH at which 50% of the drug will be ionized, but not whether it behaves as an acid or a base.
• Acidicfunctional groups most commonly found in drugs are carboxylic acids and phenols .
• Basic functional groups most commonly found in drugs are amines (nitrogen lone pair must be available for interaction with protons).
• Acidic drugs will be ionized at higher pH (and thus more aqueous soluble) and in their free acid form at lower pH (and thus more non-polar soluble).
• Basic drugs will be ionized at lower pH (and thus more aqueous soluble) and in their free base form at higher pH (and thus more non-polar soluble).
• The organic solvent you intend to inject should be immiscible with your acid/base solution.
• Refer to the densities of your solvents to determine if your organic layer will be on top or bottom.
• Place ~1 mg of the unknown in the test tube
• Note: drugs within a given class (opiates, amphetamines, etc.) usually tend to exhibit similar acid/base properties
• Alternately – you can do both acid and base extractions in separate vessels and combine the organic layers (if same solvent used), though this could potentially result in over-dilution in some cases
• Add ~1 mL of the appropriate acid/base solution
• Add ~1 mL of organic solvent
• Mix gently and allow layers to separate
• Transfer the the organic layer to the GC vial
• Dilute as instructed if needed
• Determine how much internal standard solution you need to add for the desired final concentration indicated on the board and add it

GC-MS Analysis

Your control mix will be pre-prepared and the contents detailed on the board. Your instrumental method will be pre-prepared as well, but you should make sure to note the details of the GC and MS settings provided by your TAs for your methods section.

• Run the control mix
• Review RTs and fragments
• may be omitted at TA discretion
• Run unknown samples
• Unless instructed otherwise
• Look over RTs and fragments for unknowns
• Obtain CSVs of chromatograms and spectra to plot for your report

Supplies (Part 4)

• Unknown samples

FTIR of unknowns

We may also be interested in using FTIR to confirm the identity of unknown drugs.

• Obtain blank spectrum
• Analyze unknowns
• Include photos of all samples
• Be sure pharmaceutical identifiers are as clear as possible
• Use CSVs or other raw data to plot your data in Excel or similar
• Include reference spectra and spectra for your unknown samples with explanation of stretches

Forensic Chemistry Laboratory Manual Copyright © 2022 by University of North Texas is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Field experiments, explained

Editor’s note: This is part of a series called “The Day Tomorrow Began,” which explores the history of breakthroughs at UChicago.  Learn more here.

A field experiment is a research method that uses some controlled elements of traditional lab experiments, but takes place in natural, real-world settings. This type of experiment can help scientists explore questions like: Why do people vote the way they do? Why do schools fail? Why are certain people hired less often or paid less money?

University of Chicago economists were early pioneers in the modern use of field experiments and conducted innovative research that impacts our everyday lives—from policymaking to marketing to farming and agriculture.  

Jump to a section:

What is a field experiment, why do a field experiment, what are examples of field experiments, when did field experiments become popular in modern economics, what are criticisms of field experiments.

Field experiments bridge the highly controlled lab environment and the messy real world. Social scientists have taken inspiration from traditional medical or physical science lab experiments. In a typical drug trial, for instance, participants are randomly assigned into two groups. The control group gets the placebo—a pill that has no effect. The treatment group will receive the new pill. The scientist can then compare the outcomes for each group.

A field experiment works similarly, just in the setting of real life.

It can be difficult to understand why a person chooses to buy one product over another or how effective a policy is when dozens of variables affect the choices we make each day. “That type of thinking, for centuries, caused economists to believe you can't do field experimentation in economics because the market is really messy,” said Prof. John List, a UChicago economist who has used field experiments to study everything from how people use  Uber and  Lyft to  how to close the achievement gap in Chicago-area schools . “There are a lot of things that are simultaneously moving.”

The key to cleaning up the mess is randomization —or assigning participants randomly to either the control group or the treatment group. “The beauty of randomization is that each group has the same amount of bad stuff, or noise or dirt,” List said. “That gets differenced out if you have large enough samples.”

Though lab experiments are still common in the social sciences, field experiments are now often used by psychologists, sociologists and political scientists. They’ve also become an essential tool in the economist’s toolbox.  

Some issues are too big and too complex to study in a lab or on paper—that’s where field experiments come in.

In a laboratory setting, a researcher wants to control as many variables as possible. These experiments are excellent for testing new medications or measuring brain functions, but they aren’t always great for answering complex questions about attitudes or behavior.

Labs are highly artificial with relatively small sample sizes—it’s difficult to know if results will still apply in the real world. Also, people are aware they are being observed in a lab, which can alter their behavior. This phenomenon, sometimes called the Hawthorne effect, can affect results.

Traditional economics often uses theories or existing data to analyze problems. But, when a researcher wants to study if a policy will be effective or not, field experiments are a useful way to look at how results may play out in real life.

In 2019, UChicago economist Michael Kremer (then at Harvard) was awarded the Nobel Prize alongside Abhijit Banerjee and Esther Duflo of MIT for their groundbreaking work using field experiments to help reduce poverty . In the 1990s and 2000s, Kremer conducted several randomized controlled trials in Kenyan schools testing potential interventions to improve student performance. 

In the 1990s, Kremer worked alongside an NGO to figure out if buying students new textbooks made a difference in academic performance. Half the schools got new textbooks; the other half didn’t. The results were unexpected—textbooks had no impact.

“Things we think are common sense, sometimes they turn out to be right, sometimes they turn out to be wrong,” said Kremer on an episode of  the Big Brains podcast. “And things that we thought would have minimal impact or no impact turn out to have a big impact.”

In the early 2000s, Kremer returned to Kenya to study a school-based deworming program. He and a colleague found that providing deworming pills to all students reduced absenteeism by more than 25%. After the study, the program was scaled nationwide by the Kenyan government. From there it was picked up by multiple Indian states—and then by the Indian national government.

“Experiments are a way to get at causal impact, but they’re also much more than that,” Kremer said in  his Nobel Prize lecture . “They give the researcher a richer sense of context, promote broader collaboration and address specific practical problems.”    

Among many other things, field experiments can be used to:

Study bias and discrimination

A 2004 study published by UChicago economists Marianne Bertrand and Sendhil Mullainathan (then at MIT) examined racial discrimination in the labor market. They sent over 5,000 resumes to real job ads in Chicago and Boston. The resumes were exactly the same in all ways but one—the name at the top. Half the resumes bore white-sounding names like Emily Walsh or Greg Baker. The other half sported African American names like Lakisha Washington or Jamal Jones. The study found that applications with white-sounding names were 50% more likely to receive a callback.

Examine voting behavior

Political scientist Harold Gosnell , PhD 1922, pioneered the use of field experiments to examine voting behavior while at UChicago in the 1920s and ‘30s. In his study “Getting out the vote,” Gosnell sorted 6,000 Chicagoans across 12 districts into groups. One group received voter registration info for the 1924 presidential election and the control group did not. Voter registration jumped substantially among those who received the informational notices. Not only did the study prove that get-out-the-vote mailings could have a substantial effect on voter turnout, but also that field experiments were an effective tool in political science.

Test ways to reduce crime and shape public policy

Researchers at UChicago’s  Crime Lab use field experiments to gather data on crime as well as policies and programs meant to reduce it. For example, Crime Lab director and economist Jens Ludwig co-authored a  2015 study on the effectiveness of the school mentoring program  Becoming a Man . Developed by the non-profit Youth Guidance, Becoming a Man focuses on guiding male students between 7th and 12th grade to help boost school engagement and reduce arrests. In two field experiments, the Crime Lab found that while students participated in the program, total arrests were reduced by 28–35%, violent-crime arrests went down by 45–50% and graduation rates increased by 12–19%.

The earliest field experiments took place—literally—in fields. Starting in the 1800s, European farmers began experimenting with fertilizers to see how they affected crop yields. In the 1920s, two statisticians, Jerzy Neyman and Ronald Fisher, were tasked with assisting with these agricultural experiments. They are credited with identifying randomization as a key element of the method—making sure each plot had the same chance of being treated as the next.

The earliest large-scale field experiments in the U.S. took place in the late 1960s to help evaluate various government programs. Typically, these experiments were used to test minor changes to things like electricity pricing or unemployment programs.

Though field experiments were used in some capacity throughout the 20th century, this method didn’t truly gain popularity in economics until the 2000s. Kremer and List were early pioneers and first began experimenting with the method in the 1990s.

In 2004, List co-authored  a seminal paper defining field experiments and arguing for the importance of the method. In 2008,  he and UChicago economist Steven Levitt published another study tracing the history of field experiments and their impact on economics.

In the past few decades, the use of field experiments has exploded. Today, economists often work alongside NGOs or nonprofit organizations to study the efficacy of programs or policies. They also partner with companies to test products and understand how people use services.  

There are several  ethical discussions happening among scholars as field experiments grow in popularity. Chief among them is the issue of informed consent. All studies that involve human test subjects must be approved by an institutional review board (IRB) to ensure that people are protected.

However, participants in field experiments often don’t know they are in an experiment. While an experiment may be given the stamp of approval in the research community, some argue that taking away peoples’ ability to opt out is inherently unethical. Others advocate for stricter review processes as field experiments continue to evolve.

According to List, another major issue in field experiments is the issue of scale . Many experiments only test small groups—say, dozens to hundreds of people. This may mean the results are not applicable to broader situations. For example, if a scientist runs an experiment at one school and finds their method works there, does that mean it will also work for an entire city? Or an entire country?

List believes that in addition to testing option A and option B, researchers need a third option that accounts for the limitations that come with a larger scale. “Option C is what I call critical scale features. I want you to bring in all of the warts, all of the constraints, whether they're regulatory constraints, or constraints by law,” List said. “Option C is like your reality test, or what I call policy-based evidence.”

This problem isn’t unique to field experiments, but List believes tackling the issue of scale is the next major frontier for a new generation of economists.

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• Knowledge Base

Methodology

• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

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
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

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.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

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.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

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.

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Step 2: Preclinical Research

Before testing a drug in people, researchers must find out whether it has the potential to cause serious harm, also called toxicity. The two types of preclinical research are:

FDA requires researchers to use good laboratory practices (GLP), defined in medical product development regulations, for preclinical laboratory studies.  The GLP regulations are found in 21 CFR Part 58.1: Good Laboratory Practice for Nonclinical Laboratory Studies . These regulations set the minimum basic requirements for:

study conduct

written protocols

operating procedures

study reports

and a system of quality assurance oversight for each study to help assure the safety of FDA-regulated product

Usually, preclinical studies are not very large. However, these studies must provide detailed information on dosing and toxicity levels. After preclinical testing, researchers review their findings and decide whether the drug should be tested in people.

CREME: A New AI-Powered Virtual Lab to Help Cure Genetic Diseases

CREME, an AI-powered virtual lab, developed at Cold Spring Harbor Laboratory, offers a revolutionary approach to genetic research by simulating CRISPR interference (CRISPRi).

This tool enables scientists to perform virtual genetic experiments and predict their effects on gene activity, which significantly reduces the time and resources required for lab work. CREME’s insights into gene regulation could greatly enhance drug discovery and provide access to researchers without physical labs.

Revolutionizing Genetics With AI: Introducing CREME

Imagine you’re looking at millions upon millions of mysterious genetic mutations. With CRISPR gene-editing technology, a select few of these mutations might have therapeutic potential. However, proving it would mean many thousands of hours of lab work. Just figuring out which ones are worth exploring further would take a lot of time and money. But what if you could do it in the virtual realm with artificial intelligence ?

CREME is a new AI-powered virtual laboratory invented by Cold Spring Harbor Laboratory (CSHL) Assistant Professor Peter Koo and his team. It allows geneticists to run thousands of virtual experiments with the click of a button. Now, scientists can use it to begin identifying and understanding key regions of the genome.

CRISPRi and CREME: Simulating Genetic Changes

The program is modeled after CRISPR interference (CRISPRi), a genetic perturbation technique based on CRISPR. CRISPRi allows biologists to turn down the activity of specific genes in a cell. CREME lets scientists make similar changes in the virtual genome and predicts their effects on gene activity. In other words, it’s almost like an AI version of CRISPRi.

“In reality, CRISPRi is incredibly challenging to perform in the laboratory. And you’re limited by the number of perturbations and the scale. But since we’re doing all our perturbations [virtually], we can push the boundaries. And the scale of experiments that we performed is unprecedented—hundreds of thousands of perturbation experiments,” explains Koo.

Delving Into Genome Analysis With AI

Koo and his team tested CREME on another AI-powered genome analysis tool called Enformer. They wanted to know how Enformer’s algorithm makes predictions about the genome. Questions like that are central to Koo’s work, he says.

“We have these big, powerful models. They’re quite compelling at taking DNA sequences and predicting gene expression. But we don’t really have any good ways of trying to understand what these models are learning. Presumably, they’re making accurate predictions because they’ve learned a lot of the rules about gene regulation, but we don’t actually know what their predictions are based off of.”

Implications for Drug Discovery and Accessibility

With CREME, Koo’s team uncovered a series of genetic rules that Enformer learned while analyzing the genome. That insight may one day prove invaluable for drug discovery. “Understanding the rules of gene regulation gives you more options for tuning gene expression levels in precise and predictable ways,” says Koo.

With further fine-tuning, CREME may soon set geneticists on the path to discovering new therapeutic targets. Perhaps most impactfully, it may even give scientists who do not have access to a real laboratory the power to make these breakthroughs.

Reference: 16 September 2024, Nature Genetics . DOI: 10.1038/s41588-024-01923-3

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