Weekend batch
Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.
Free eBook: Top Programming Languages For A Data Scientist
Normality Test in Minitab: Minitab with Statistics
Machine Learning Career Guide: A Playbook to Becoming a Machine Learning Engineer
Content preview.
Arcu felis bibendum ut tristique et egestas quis:
10.1 - setting the hypotheses: examples.
A significance test examines whether the null hypothesis provides a plausible explanation of the data. The null hypothesis itself does not involve the data. It is a statement about a parameter (a numerical characteristic of the population). These population values might be proportions or means or differences between means or proportions or correlations or odds ratios or any other numerical summary of the population. The alternative hypothesis is typically the research hypothesis of interest. Here are some examples.
About 10% of the human population is left-handed. Suppose a researcher at Penn State speculates that students in the College of Arts and Architecture are more likely to be left-handed than people found in the general population. We only have one sample since we will be comparing a population proportion based on a sample value to a known population value.
A generic brand of the anti-histamine Diphenhydramine markets a capsule with a 50 milligram dose. The manufacturer is worried that the machine that fills the capsules has come out of calibration and is no longer creating capsules with the appropriate dosage.
Many people are starting to prefer vegetarian meals on a regular basis. Specifically, a researcher believes that females are more likely than males to eat vegetarian meals on a regular basis.
Obesity is a major health problem today. Research is starting to show that people may be able to lose more weight on a low carbohydrate diet than on a low fat diet.
This research question might also be addressed like example 11.4 by making the hypotheses about comparing the proportion of stroke patients that live with smokers to the proportion of controls that live with smokers.
Harvard Business School Online's Business Insights Blog provides the career insights you need to achieve your goals and gain confidence in your business skills.
Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.
If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.
Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.
Access your free e-book today.
To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.
A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”
Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.
When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.
The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.
As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.
In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.
Related: 9 Fundamental Data Science Skills for Business Professionals
1. alternative hypothesis and null hypothesis.
In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.
For example, consider a company’s leadership team that historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.
In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”
The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.
Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.
Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.
With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.
In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.
When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.
Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.
To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.
A survey involves asking a series of questions to a random population sample and recording self-reported responses.
Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.
Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.
Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.
If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.
Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .
The bottom line.
Hypothesis testing, sometimes called significance testing, is an act in statistics whereby an analyst tests an assumption regarding a population parameter. The methodology employed by the analyst depends on the nature of the data used and the reason for the analysis.
Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data. Such data may come from a larger population or a data-generating process. The word "population" will be used for both of these cases in the following descriptions.
In hypothesis testing, an analyst tests a statistical sample, intending to provide evidence on the plausibility of the null hypothesis. Statistical analysts measure and examine a random sample of the population being analyzed. All analysts use a random population sample to test two different hypotheses: the null hypothesis and the alternative hypothesis.
The null hypothesis is usually a hypothesis of equality between population parameters; e.g., a null hypothesis may state that the population mean return is equal to zero. The alternative hypothesis is effectively the opposite of a null hypothesis. Thus, they are mutually exclusive , and only one can be true. However, one of the two hypotheses will always be true.
The null hypothesis is a statement about a population parameter, such as the population mean, that is assumed to be true.
If an individual wants to test that a penny has exactly a 50% chance of landing on heads, the null hypothesis would be that 50% is correct, and the alternative hypothesis would be that 50% is not correct. Mathematically, the null hypothesis is represented as Ho: P = 0.5. The alternative hypothesis is shown as "Ha" and is identical to the null hypothesis, except with the equal sign struck-through, meaning that it does not equal 50%.
A random sample of 100 coin flips is taken, and the null hypothesis is tested. If it is found that the 100 coin flips were distributed as 40 heads and 60 tails, the analyst would assume that a penny does not have a 50% chance of landing on heads and would reject the null hypothesis and accept the alternative hypothesis.
If there were 48 heads and 52 tails, then it is plausible that the coin could be fair and still produce such a result. In cases such as this where the null hypothesis is "accepted," the analyst states that the difference between the expected results (50 heads and 50 tails) and the observed results (48 heads and 52 tails) is "explainable by chance alone."
Some statisticians attribute the first hypothesis tests to satirical writer John Arbuthnot in 1710, who studied male and female births in England after observing that in nearly every year, male births exceeded female births by a slight proportion. Arbuthnot calculated that the probability of this happening by chance was small, and therefore it was due to “divine providence.”
Hypothesis testing helps assess the accuracy of new ideas or theories by testing them against data. This allows researchers to determine whether the evidence supports their hypothesis, helping to avoid false claims and conclusions. Hypothesis testing also provides a framework for decision-making based on data rather than personal opinions or biases. By relying on statistical analysis, hypothesis testing helps to reduce the effects of chance and confounding variables, providing a robust framework for making informed conclusions.
Hypothesis testing relies exclusively on data and doesn’t provide a comprehensive understanding of the subject being studied. Additionally, the accuracy of the results depends on the quality of the available data and the statistical methods used. Inaccurate data or inappropriate hypothesis formulation may lead to incorrect conclusions or failed tests. Hypothesis testing can also lead to errors, such as analysts either accepting or rejecting a null hypothesis when they shouldn’t have. These errors may result in false conclusions or missed opportunities to identify significant patterns or relationships in the data.
Hypothesis testing refers to a statistical process that helps researchers determine the reliability of a study. By using a well-formulated hypothesis and set of statistical tests, individuals or businesses can make inferences about the population that they are studying and draw conclusions based on the data presented. All hypothesis testing methods have the same four-step process, which includes stating the hypotheses, formulating an analysis plan, analyzing the sample data, and analyzing the result.
Sage. " Introduction to Hypothesis Testing ," Page 4.
Elder Research. " Who Invented the Null Hypothesis? "
Formplus. " Hypothesis Testing: Definition, Uses, Limitations and Examples ."
An official website of the United States government
Here’s how you know
Official websites use .gov A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
The following list, provided by CBP, is a guide to acceptable and unacceptable descriptions. This list is not exhaustive and will continue to expand as unacceptable descriptions are identified and acceptable descriptions are further refined. Descriptions in the Acceptable column should be viewed only as examples of the items they actually describe and not as a list of specifically acceptable or restrictive terms.Vague Item Description
Unacceptable | Acceptable |
---|---|
"Brand" or "Trade Mark" names by themselves, i.e., "Bubbles Brand" | "Bubbles Brand" Laundry Detergent |
Laundry Detergent | |
Animals | Horse |
Poultry | |
Bovine | |
Apparel/Clothing/Garments | Shoes, Footwear |
Wearing Apparel, Ladies' Apparel, Men's Apparel | Women's Dresses |
Men's Shirts | |
Boy's Jackets | |
Appliances | Refrigerator |
Stove | |
Microwave Oven | |
Coffee Machines | |
Accessories | Hair Elastics |
Sunglasses | |
Socks | |
Auto Parts | Air Filters |
Automobile Brakes | |
Automotive Windshield | |
Bill of Lading, Sea Waybill, Air Manifest or a reference to another bill of lading number | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Caps | Baseball Caps |
Blasting Caps | |
Bottle Caps | |
Hub Caps | |
Chemicals, hazardous | Actual Chemical Name (not brand name): |
U.N. HAZMAT Code Identifier | |
Chemicals, non-hazardous | Aluminum Potassium Sulfate |
Methyl Alcohol | |
U.N. HAZMAT Code Identifier | |
Cleaning Products | Detergents |
Mops | |
Window Cleaner | |
Company Business | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Consolidated (on house or simple bills) | Consolidated (only on master bills, where there will be several house bills of varying descriptions) |
Consumer Goods | A clear and concise description of the item is required |
Crafts/Craft Supplies | Pipe Cleaners |
Daily Necessities | A clear and concise description of the item is required (e.g. Plastic Comb, Cosmetic Mirror, Deodorant, Diapers) |
Necessities | A clear and concise description of the item is required (e.g. Shampoo, Hand Cream, Toilet Paper) |
Handicrafts | Construction paper, cotton balls, pipe cleaners, pompoms, decorative objects made by hand |
Electronic Goods | Computers, Monitors, Televisions, Mobile Telephones, DVD players, Electronic Toys, Video Game Consoles, Electronic Dolls |
Equipment | Oil Well Equipment, Hydro-Electric Turbine |
Industrial Equipment | Poultry Equipment, Compact Farm Tractor |
Film | Camera Film |
Polyethylene Film | |
Polyester Film | |
Flooring | Wood Flooring |
Carpet | |
Ceramic Tile | |
Marble Flooring | |
Foodstuffs | Packaged Rice, Bulk Rice, Mangos, Baking Flour |
Food | Pasta, Canned Tuna, Corn Tortillas |
Meat | Fresh Beef, Frozen Chicken |
Fish | Live Trout, Frozen Salmon, Canned Tuna |
Produce/Assorted Produce/Mixed produce/Mix Veg | Fresh Oranges, Frozen Broccoli, Canned Peaches |
Pet food | Canned Dog Food, Dry Cat Food |
Cases of Food | Juice, Olive Oil |
Snacks | Soda, Crackers, Potato Chips |
Gifts | Dolls, Basketball, Toy Car |
Novelty Items | Remote Control Cars, Toy Phone |
Frames | Picture Frames |
Freight Prepaid | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Household Goods/Personal Effects | Acceptable only when goods are Personal / Used Household Effects transported by a commercial carrier (Goods accounted under a Personal Effects Accounting Document) |
Indecipherable descriptions ex. "RED SMOOTH MODULAR", "CDRE", "D6T PARTS", "RIPE”, “Z”, “2” | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Iron | Iron Pipes |
Iron Plates | |
Leather Articles | Saddles |
Leather Handbags | |
Leather Jackets | |
Shoes | |
Machines | Sewing Machines |
Printing Machines | |
Machine Parts | Oil Pumps |
Silicone Seals | |
Car Engines | |
Medical Supplies | Medical Gloves, N95 Masks, Rolls of Gauze |
Biologicals | Blood, plasma, tissue, semen |
Medical Device | Dialysis Machine |
Laboratory Goods | Syringes |
Glass vials for laboratory use | |
Medication/Pharmaceuticals | Insulin, Allergy Medication, with the common name or chemical name |
Metal | Ingots of metal (precious or otherwise) |
Round bars of steel or other metal | |
Deformed bars/rebars (of metal) | |
Plates (of metal) | |
Billets (of metal) | |
Slabs (of metal) | |
Pipes (of metal) | |
Beams (of metal) | |
Tubes/Tubing (of metal) | |
Angles, shapes and sections (of metal) | |
Sheets (of metal) | |
Expanded metal | |
Flat bars (of metal) | |
Strand wire (of metal) | |
Oil | Mineral Oil |
Motor Oil | |
Olive Oil | |
Online Retailer, Online Retailer Shipment | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Ore | Iron Ore |
Copper Ore | |
Packaging | Corrugated Cardboard Boxes |
Boxes | Mailing Envelopes |
Cartons | Plastic Bubble Wrap |
Palletized Shipment | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Paper | Paper Rolls |
Wrapping Paper | |
Paper Pulp | |
Paper Towel | |
Printing Paper | |
Pipes | Plastic Pipes |
PVC Pipes | |
Steel Pipes | |
Plants/Flowers | Tulips, Daisies, Roses |
Cuttings | Cedar Saplings |
Tomato Plants | |
Plastic Goods | Plastic Kitchenware |
Industrial Plastics | Plastic Toys |
Plastic Sheets | |
Plastic Tubes | |
Polyurethane | Polyurethane Threads |
Polyurethane Medical Gloves | |
Powder | Flea Powder |
Baby Powder | |
Corn Starch | |
Promotional Items | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Pump | Oil Pump |
Water Pump | |
Bicycle Pump | |
Rubber Articles | Rubber Hoses |
Tires | |
Rubber Toys | |
Rubber Conveyor Belts | |
Rods | Welding Rods |
Rebar | |
Aluminum Rods | |
Reactor Rods | |
Sample | Shampoo Sample |
Conditioner Sample | |
Makeup Sample | |
Scrap | Plastic Scrap |
Aluminum Scrap | |
Iron Scrap | |
Serial Number only ("SN HAFR997MJ02041010") | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Shingles | Asphalt Shingles |
Steel Shingles | |
Wood Shingles | |
Spare Parts | Cellphone Replacement Screen |
New Automobile Breaks | |
Sporting Goods | Hockey Sticks |
Soccer Balls | |
Goal Nets | |
STC (Said to Contain) | A clear and concise description of the item is required – see other acceptable descriptions for examples |
General Cargo | A clear and concise description of the item is required – see other acceptable descriptions for examples |
FAK (Freight of All Kinds) | A clear and concise description of the item is required – see other acceptable descriptions for examples |
"No Description" | A clear and concise description of the item is required – see other acceptable descriptions for examples |
'Misc/Miscellaneous' | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Various | A clear and concise description of the item is required – see other acceptable descriptions for examples |
General | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Unknown | A clear and concise description of the item is required – see other acceptable descriptions for examples |
UNK | A clear and concise description of the item is required – see other acceptable descriptions for examples |
XX | A clear and concise description of the item is required – see other acceptable descriptions for examples |
NOI (Not Otherwise Indicated) | A clear and concise description of the item is required – see other acceptable descriptions for examples |
NES (Note Elsewhere Specified) | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Steel | Steel Plates |
Steel Coils | |
Supplements | Vitamins |
Protein Powder | |
Supplies | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Stuff | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Taxable Groceries | Ice Cream |
Potato Chips | |
Textiles | Carpets/Rugs |
Silk | |
Finished Fabric Rolls | |
Thing | A clear and concise description of the item is required – see other acceptable descriptions for examples |
Tiles | Marble Tiles |
Toiletries/Bathroom Products | Towels |
Toothbrushes | |
Shampoo | |
Tools | Screwdrivers, Wrenches, Hammers |
Cordless Drills, Circular Saws, Wired Impact Drivers, Pneumatic Ratchets | |
Industrial Lathe, Band Saw, Reciprocating Saw | |
Toys/Games | Wooden Children's Toys |
Plastic Children's Toys | |
Board Games | |
Console Games | |
Vehicles | Cars, Trucks, Buses, Recreational Vehicle |
Tractors, Combines | |
Bicycles | |
Boats | |
Wires | Steel Wire |
Copper Wire | |
Auto Harness | |
Coiled Wire (Industrial) | |
Wood | Hemlock logs with bark |
Empty Wood Pallets | |
Cut Lumber |
IMAGES
COMMENTS
A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.
A hypothesis is a tentative statement about the relationship between two or more variables. Explore examples and learn how to format your research hypothesis.
Use this guide to learn how to write a hypothesis and read successful and unsuccessful examples of a testable hypotheses.
Definition: Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation. Hypothesis is often used in scientific research to guide the design of experiments ...
A research hypothesis is an assumption or a tentative explanation for a specific process observed during research. Unlike a guess, research hypothesis is a calculated, educated guess proven or disproven through research methods.
Research begins with a research question and a research hypothesis. But what are the characteristics of a good hypothesis? In this article, we dive into the types of research hypothesis, explain how to write a research hypothesis, offer research hypothesis examples and answer top FAQs on research hypothesis. Read more!
A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection. Example: Hypothesis
A research hypothesis explains a phenomenon or the relationships between variables in the real world. See good and bad hypothesis examples.
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.
Examples. A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
What Is a Hypothesis? Merriam Webster defines a hypothesis as "an assumption or concession made for the sake of argument." In other words, a hypothesis is an educated guess. Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it's true or not. Keep in mind that in science, a hypothesis should be testable. You have to be able to design an ...
This article shares several examples of hypothesis testing in real life situations.
hypothesis. science. scientific hypothesis, an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an "If…then" statement summarizing the idea and in the ...
A hypothesis is an educated guess about what you think will happen in a scientific experiment, based on your observations. Before conducting the experiment, you propose a hypothesis so that you can determine if your prediction is supported. Read More Crafting Hypotheses in Science By Anne Marie Helmenstine, Ph.D.
Here are examples of a scientific hypothesis and how to improve a hypothesis to use it for an experiment.
Learn what a hypothesis is, how to write one that effectively tests the relationship between two or more things and then use the examples to create your own.
A null hypothesis and an alternative hypothesis are set up before performing the hypothesis testing. This helps to arrive at a conclusion regarding the sample obtained from the population. In this article, we will learn more about hypothesis testing, its types, steps to perform the testing, and associated examples.
What is Hypothesis? A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.
Learn about hypothesis testing in statistics with our detailed walkthrough, perfect for students and professionals looking to improve their statistical skills.
What does hypothesis mean? Learn the hypothesis definition in this easy-to-follow lesson. Take an in-depth look at hypothesis examples and the...
10.1 - Setting the Hypotheses: Examples A significance test examines whether the null hypothesis provides a plausible explanation of the data. The null hypothesis itself does not involve the data. It is a statement about a parameter (a numerical characteristic of the population). These population values might be proportions or means or differences between means or proportions or correlations ...
Hypothesis testing, then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.
Hypothesis testing is an act in statistics whereby an analyst tests an assumption regarding a population parameter. The methodology employed by the analyst depends on the nature of the data used ...
The following list, provided by CBP, is a guide to acceptable and unacceptable descriptions. This list is not exhaustive and will continue to expand as unacceptable descriptions are identified and acceptable descriptions are further refined. Descriptions in the Acceptable column should be viewed only as examples of the items they actually describe and not as a list of specifically acceptable ...