Hypothesis Examples

A hypothesis is a prediction in the scientific method. (NASA/GSFC/Chris Gunn)

A hypothesis is a prediction of the outcome of a test. It forms the basis for designing an experiment in the scientific method. A good hypothesis is testable, meaning it makes a prediction you can check with observation or testing. Here are different hypothesis examples.

Null Hypothesis Examples

The null hypothesis (H0) is also known as the zero-difference or no-difference hypothesis. It predicts that changing one variable (independent variable) will have no effect on the variable being measured (dependent variable).

• Plant growth is unaffected by temperature.
• If you increase temperature, then solubility of salt will increase.
• Incidence of skin cancer is unrelated to ultraviolet light exposure.
• All brands of light bulb last equally long.
• Cats have no preference for the color of cat food.
• All daisies have the same number of petals.

Sometimes the null hypothesis is used to show there is a correlation between two variables. For example, if you suspect plant growth is affected by temperature, you could state the null hypothesis. Why would you do this, rather than say “If you change temperature, plant growth will be affected”? The answer is because it’s easier to apply a statistical test to show, with a high level of confidence, a null hypothesis is correct or incorrect.

Research Hypothesis Examples

A research hypothesis (H1) is a type of hypothesis used to design an experiment. This type of hypothesis is often written as an if-then statement because it’s easy to identify the independent and dependent variables and see how one affects the other. If-then statements are used to explore cause and effect. In other cases, the hypothesis is stated to show a correlation between two variables. Here are some research hypothesis examples:

• If you leave the lights on, then it will take longer for people to fall asleep.
• If you refrigerate apples, they will last longer before going bad.
• If you keep the curtains closed, then less electricity will be used to heat or cool the house (electric bill will be lower).
• If you leave a bucket of water uncovered, then it will evaporate more quickly.
• Goldfish lose their color if they are not exposed to light.
• Workers who take vacations are more productive than those who never take time off.

Is It Okay To Disprove a Hypothesis?

Yes! You may even choose to write your hypothesis in such a way that it can be disproved because it’s easier to prove a statement is wrong than to prove it is right. In other cases, if your prediction is incorrect, that doesn’t mean the science is bad. Revising a hypothesis is common. It demonstrates you learned something you did not know before you conducted the experiment.

Test Yourself with a Scientific Method Quiz

Steps of the Scientific Method

Scientific Method Steps (sciencenotes.org)

The scientific method is a system scientists and other people use to ask and answer questions about the natural world. In a nutshell, the scientific method works by making observations, asking a question or identifying a problem, and then designing and analyzing an experiment to test a prediction of what you expect will happen. It’s a powerful analytical tool because once you draw conclusions, you may be able to answer a question and make predictions about future events.

These are the steps of the scientific method:

• Make observations.

Sometimes this step is omitted in the list, but you always make observations before asking a question, whether you recognize it or not. You always have some background information about a topic. However, it’s a good idea to be systematic about your observations and to record them in a lab book or another way. Often, these initial observations can help you identify a question. Later on, this information may help you decide on another area of investigation of a topic.

• Ask a question, identify a problem, or state an objective.

There are various forms of this step. Sometimes you may want to state an objective and a problem and then phrase it in the form of a question. The reason it’s good to state a question is because it’s easiest to design an experiment to answer a question. A question helps you form a hypothesis, which focuses your study.

• Research the topic.

You should conduct background research on your topic to learn as much as you can about it. This can occur both before and after you state an objective and form a hypothesis. In fact, you may find yourself researching the topic throughout the entire process.

• Formulate a hypothesis.

A hypothesis is a formal prediction. There are two forms of a hypothesis that are particularly easy to test. One is to state the hypothesis as an “if, then” statement. An example of an if-then hypothesis is: “If plants are grown under red light, then they will be taller than plants grown under white light.” Another good type of hypothesis is what is called a “null hypothesis” or “no difference” hypothesis. An example of a null hypothesis is: “There is no difference in the rate of growth of plants grown under red light compared with plants grown under white light.”

• Design and perform an experiment to test the hypothesis.

Once you have a hypothesis, you need to find a way to test it. This involves an experiment. There are many ways to set up an experiment. A basic experiment contains variables, which are factors you can measure. The two main variables are the independent variable (the one you control or change) and the dependent variable (the one you measure to see if it is affected when you change the independent variable).

• Record and analyze the data you obtain from the experiment.

It’s a good idea to record notes alongside your data, stating anything unusual or unexpected. Once you have the data, draw a chart, table, or graph to present your results. Next, analyze the results to understand what it all means.

• Determine whether you accept or reject the hypothesis.

Do the results support the hypothesis or not? Keep in mind, it’s okay if the hypothesis is not supported, especially if you are testing a null hypothesis. Sometimes excluding an explanation answers your question! There is no “right” or “wrong” here. However, if you obtain an unexpected result, you might want to perform another experiment.

• Draw a conclusion and report the results of the experiment.

What good is knowing something if you keep it to yourself? You should report the outcome of the experiment, even if it’s just in a notebook. What did you learn from the experiment?

How Many Steps Are There?

You may be asked to list the 5 steps of the scientific method or the 6 steps of the method or some other number. There are different ways of grouping together the steps outlined here, so it’s a good idea to learn the way an instructor wants you to list the steps. No matter how many steps there are, the order is always the same.

Calculate Percent Error

Percent error is the percent difference between a measured and expected value. (image: Sherman Geronimo-Tan)

Percent Error Definition

Percent error, sometimes referred to as percentage error, is an expression of the difference between a measured value and the known or accepted value. It is often used in science to report the difference between experimental values and expected values.

The formula for calculating percent error is:

Note: occasionally, it is useful to know if the error is positive or negative. If you need to know the positive or negative error, this is done by dropping the absolute value brackets in the formula. In most cases, absolute error is fine. For example, in experiments involving yields in chemical reactions, it is unlikely you will obtain more product than theoretically possible.

Steps to Calculate the Percent Error

1. Subtract the accepted value from the experimental value.
2. Take the absolute value of step 1
3. Divide that answer by the accepted value.
4. Multiply that answer by 100 and add the % symbol to express the answer as a percentage.

Now let’s try an example problem.

You are given a cube of pure copper. You measure the sides of the cube to find the volume and weigh it to find its mass. When you calculate the density using your measurements, you get 8.78 grams/cm3. Copper’s accepted density is 8.96 g/cm3. What is your percent error?

Solution:
experimental value = 8.78 g/cm3
accepted value = 8.96 g/cm3

Step 1: Subtract the accepted value from the experimental value.

8.96 g/cm3 – 8.78 g/cm3 = -0.18 g/cm3

Step 2: Take the absolute value of step 1

|-0.18 g/cm3| = 0.18 g/cm3

Step 3: Divide that answer by the accepted value.

Step 4: Multiply that answer by 100 and add the % symbol to express the answer as a percentage.

0.02 x 100 = 2
2%

The percent error of your density calculation was 2%.

New Method To Fight Cholesterol

Pair of laboratory mice. The left hand mouse is significantly fatter than the mouse on the right. High cholesterol levels are often linked to obesity. Credit: Oak Ridge National Laboratory

Atherosclerosis is a medical condition where fatty materials, such as cholesterol, build up on the walls of blood vessels. Arteries begin to stiffen and narrow, reducing blood flow and increasing blood pressure. This the main cause of heart attacks and strokes and one of the leading causes of death in humans.

Scientists at Johns Hopkins identified a glycosphingolipid molecule (GSL) as the main culprit responsible for atherosclerosis. GSL is found in the membranes of all cells and is responsible for regulating cell growth. Turns out, it also has role in the regulation of the way our bodies utilize, transport and purge cholesterol.

A series of experiments using mice and rabbits found that when GSL synthesis is blocked using a compound called D-PDMP (D-threo-1-Phenyl-2-Decanoylamino-3-Morpholino-1-Propanol):

• LDL (low density lipoprotein), the ‘bad’ cholesterol levels dropped
• oxidized LDLs, fats formed when LDLs react with free radicals, levels dropped
• HDL (High density lipoprotein), the ‘good’ cholesterol levels rose
• Triglyceride, another fat building material, levels dropped

The first experiment used mice genetically predisposed to atherosclerosis and fed a high fat, cholesterol packed diet. This would practically guarantee the mice would develop the disease. One third of the animals were given a low dose of a drug containing D-PDMP. A second third received a double dose of the drug and the last group received a placebo.

After several months of this diet, the aortas of the mice were measured. The placebo group developed thick buildups of fat typical of atherosclerosis. The low-dose group aortas had significantly fewer fat deposits and the high-dose group had aortas that were virtually clear of any fat buildup. Scientists also monitored blood flow in the mice and found the D-PDMP groups had normal blood flow and the placebo group had reduced blood flow. Examination of the livers showed the dosed mice had increased levels of the enzymes responsible for maintaining the balance and purging of fats in the body.

Another experiment fed two groups of healthy rabbits a high fat diet. The placebo group developed fat buildups in their arteries, narrowed blood vessels, and cholesterol levels that increased 17-fold. The group dosed with the D-PDMP drug remained normal and healthy.

While this is great news for mice and bunnies, it remains to be seen if the same is true for humans. Human testing is a logical next step. If it works, it could help the many people who run the risk of high cholesterol.

These experiments are reported online on April 7, 2014 in the American Heart Association journal Circulation.