There are 13 different types of hypothesis. These include simple, complex, null, alternative, composite, directional, non-directional, logical, empirical, statistical, associative, exact, and inexact.
A hypothesis can be categorized into one or more of these types. However, some are mutually exclusive and opposites. Simple and complex hypotheses are mutually exclusive, as are direction and non-direction, and null and alternative hypotheses.
Below I explain each hypothesis in simple terms for absolute beginners. These definitions may be too simple for some, but they’re designed to be clear introductions to the terms to help people wrap their heads around the concepts early on in their education about research methods.
Types of Hypothesis
1. Simple Hypothesis
A simple hypothesis is a hypothesis that predicts a correlation between two test variables: an independent and a dependent variable.
This is the easiest and most straightforward type of hypothesis. You simply need to state an expected correlation between the dependant variable and the independent variable.
You do not need to predict causation (see: directional hypothesis). All you would need to do is prove that the two variables are linked.
Simple Hypothesis Examples
|Do people over 50 like Coca-Cola more than people under 50?||On average, people over 50 like Coca-Cola more than people under 50.|
|According to national registries of car accident data, are Canadians better drivers than Americans?||Canadians are better drivers than Americans.|
|Are carpenters more liberal than plumbers?||Carpenters are more liberal than plumbers.|
|Do guitarists live longer than pianists?||Guitarists do live longer than pianists.|
|Do dogs eat more in summer than winter?||Dogs do eat more in summer than winter.|
2. Complex Hypothesis
A complex hypothesis is a hypothesis that contains multiple variables, making the hypothesis more specific but also harder to prove.
You can have multiple independent and dependant variables in this hypothesis.
Complex Hypothesis Example
|Do (1) age and (2) weight affect chances of getting (3) diabetes and (4) heart disease?||(1) Age and (2) weight increase your chances of getting (3) diabetes and (4) heart disease.|
In the above example, we have multiple independent and dependent variables:
- Independent variables: Age and weight.
- Dependent variables: diabetes and heart disease.
Because there are multiple variables, this study is a lot more complex than a simple hypothesis. It quickly gets much more difficult to prove these hypotheses. This is why undergraduate and first-time researchers are usually encouraged to use simple hypotheses.
3. Null Hypothesis
A null hypothesis will predict that there will be no significant relationship between the two test variables.
For example, you can say that “The study will show that there is no correlation between marriage and happiness.”
A good way to think about a null hypothesis is to think of it in the same way as “innocent until proven guilty”. Unless you can come up with evidence otherwise, your null hypothesis will stand.
A null hypothesis may also highlight that a correlation will be inconclusive. This means that you can predict that the study will not be able to confirm your results one way or the other. For example, you can say “It is predicted that the study will be unable to confirm a correlation between the two variables due to foreseeable interference by a third variable.”
Beware that an inconclusive null hypothesis may be questioned by your teacher. Why would you conduct a test that you predict will not provide a clear result? Perhaps you should take a closer look at your methodology and re-examine it. Nevertheless, inconclusive null hypotheses can sometimes have merit.
Null Hypothesis Examples
|Question||Null Hypothesis (H0)|
|Do people over 50 like Coca-Cola more than people under 50?||Age has no effect on preference for Coca-Cola.|
|Are Canadians better drivers than Americans?||Nationality has no effect on driving ability.|
|Are carpenters more liberal than plumbers?||There is no statistically significant difference in political views between carpenters and plumbers.|
|Do guitarists live longer than pianists?||There is no statistically significant difference in life expectancy between guitarists and pianists.|
|Do dogs eat more in summer than winter?||Time of year has no effect on dogs’ appetites.|
4. Alternative Hypothesis
An alternative hypothesis is a hypothesis that is anything other than the null hypothesis. It will disprove the null hypothesis.
We use the symbol HA or H1 to denote an alternative hypothesis.
The null and alternative hypotheses are usually used together. We will say the null hypothesis is the case where a relationship between two variables is non-existent. The alternative hypothesis is the case where there is a relationship between those two variables.
The following statement is always true: H0 ≠ HA.
Let’s take the example of the hypothesis: “Does eating oatmeal before an exam impact test scores?”
We can have two hypotheses here:
- Null hypothesis (H0): “Eating oatmeal before an exam does not impact test scores.”
- Alternative hypothesis (HA): “Eating oatmeal before an exam does impact test scores.”
For the alternative hypothesis to be true, all we have to do is disprove the null hypothesis for the alternative hypothesis to be true. We do not need an exact prediction of how much oatmeal will impact the test scores or even if the impact is positive or negative. So long as the null hypothesis is proven to be false, then the alternative hypothesis is proven to be true.
5. Composite Hypothesis
A composite hypothesis is a hypothesis that does not predict the exact parameters, distribution, or range of the dependent variable.
Often, we would predict an exact outcome. For example: “23 year old men are on average 189cm tall.” Here, we are giving an exact parameter. So, the hypothesis is not composite.
But, often, we cannot exactly hypothesize something. We assume that something will happen, but we’re not exactly sure what. In these cases, we might say: “23 year old men are not on average 189cm tall.”
We haven’t set a distribution range or exact parameters of the average height of 23 year old men. So, we’ve introduced a composite hypothesis as opposed to an exact hypothesis.
Generally, an alternative hypothesis (discussed above) is composite because it is defined as anything except the null hypothesis. This ‘anything except’ does not define parameters or distribution, and therefore it’s an example of a composite hypothesis.
6. Directional Hypothesis
A directional hypothesis makes a prediction about the positivity or negativity of the effect of an intervention prior to the test being conducted.
Instead of being agnostic about whether the effect will be positive or negative, it nominates the effect’s directionality.
We often call this a one-tailed hypothesis (in contrast to a two-tailed or non-directional hypothesis) because, looking at a distribution graph, we’re hypothesizing that the results will lean toward one particular tail on the graph – either the positive or negative.
Directional Hypothesis Examples
|Does adding a 10c charge to plastic bags at grocery stores lead to changes in uptake of reusable bags?||Adding a 10c charge to plastic bags in grocery stores will lead to an increase in uptake of reusable bags.|
|Does a Universal Basic Income influence retail worker wages?||Universal Basic Income puts upward pressure on retail worker wages.|
|Does rainy weather impact the amount of moderate to high intensity exercise people do per week in the city of Vancouver?||Rainy weather decreases the amount of moderate to high intensity exercise people do per week in the city of Vancouver.|
|Does introducing fluoride to the water system in the city of Austin impact number of dental visits per capita per year?||Introducing fluoride to the water system in the city of Austin decreases the number of dental visits per capita per year?|
|Does giving children chocolate rewards during study time for positive answers impact standardized test scores?||Giving children chocolate rewards during study time for positive answers increases standardized test scores.|
7. Non-Directional Hypothesis
A non-directional hypothesis does not specify the predicted direction (e.g. positivity or negativity) of the effect of the independent variable on the dependent variable.
These hypotheses predict an effect, but stop short of saying what that effect will be.
A non-directional hypothesis is similar to composite and alternative hypotheses. All three types of hypothesis tend to make predictions without defining a direction. In a composite hypothesis, a specific prediction is not made (although a general direction may be indicated, so the overlap is not complete). For an alternative hypothesis, you often predict that the even will be anything but the null hypothesis, which means it could be more or less than H0 (or in other words, non-directional).
Let’s turn the above directional hypotheses into non-directional hypotheses.
Non-Directional Hypothesis Examples
|Does adding a 10c charge to plastic bags at grocery stores lead to changes in uptake of reusable bags?||Adding a 10c charge to plastic bags in grocery stores will lead to a change in uptake of reusable bags.|
|Does a Universal Basic Income influence retail worker wages?||Universal Basic Income will affect retail worker wages.|
|Does rainy weather impact the amount of moderate to high intensity exercise people do per week in the city of Vancouver?||Rainy weather will affect the amount of moderate to high intensity exercise people do per week in the city of Vancouver.|
|Does introducing fluoride to the water system in the city of Austin impact number of dental visits per capita per year?||Introducing fluoride to the water system in the city of Austin will affect the number of dental visits per capita per year?|
|Does giving children chocolate rewards during study time for positive answers impact standardized test scores?||Giving children chocolate rewards during study time for positive answers will affect standardized test scores.|
8. Logical Hypothesis
A logical hypothesis is a hypothesis that cannot be tested, but has some logical basis underpinning our assumptions.
These are most commonly used in philosophy because philosophical questions are often untestable and therefore we must rely on our logic to formulate logical theories.
Usually, we would want to turn a logical hypothesis into an empirical one through testing if we got the chance. Unfortunately, we don’t always have this opportunity because the test is too complex, expensive, or simply unrealistic.
Here are some examples:
- Before the 1980s, it was hypothesized that the Titanic came to its resting place at 41° N and 49° W, based on the time the ship sank and the ship’s presumed path across the Atlantic Ocean. However, due to the depth of the ocean, it was impossible to test. Thus, the hypothesis was simply a logical hypothesis.
- Dinosaurs closely related to Aligators probably had green scales because Aligators have green scales. However, as they are all extinct, we can only rely on logic and not empirical data.
9. Empirical Hypothesis
An empirical hypothesis is the opposite of a logical hypothesis. It is a hypothesis that is currently being tested using scientific analysis. We can also call this a ‘working hypothesis’.
We can to separate research into two types: theoretical and empirical. Theoretical research relies on logic and thought experiments. Empirical research relies on tests that can be verified by observation and measurement.
So, an empirical hypothesis is a hypothesis that can and will be tested.
Here are some examples:
- Raising the wage of restaurant servers increases staff retention.
- Adding 1 lb of corn per day to cows’ diets decreases their lifespan.
- Mushrooms grow faster at 22 degrees Celsius than 27 degrees Celsius.
Each of the above hypotheses can be tested, making them empirical rather than just logical (aka theoretical).
10. Statistical Hypothesis
A statistical hypothesis utilizes representative statistical models to draw conclusions about broader populations.
It requires the use of datasets or carefully selected representative samples so that statistical inference can be drawn across a larger dataset.
This type of research is necessary when it is impossible to assess every single possible case. Imagine, for example, if you wanted to determine if men are taller than women. You would be unable to measure the height of every man and woman on the planet. But, by conducting sufficient random samples, you would be able to predict with high probability that the results of your study would remain stable across the whole population.
You would be right in guessing that almost all qualitative research studies conducted in academic settings today involve statistical hypotheses.
Statistical Hypothesis Examples
- Human Sex Ratio. The most famous statistical hypothesis example is that of John Arbuthnot’s sex at birth case study in 1710. Arbuthnot used birth data to determine with high statistical probability that there are more male births than female births. He called this divine providence, and to this day, his findings remain true: more men are born than women.
- Lady Testing Tea. A 1935 study by Ronald Fisher involved testing a woman who believed she could tell whether milk was added before or after water to a cup of tea. Fisher gave her 4 cups in which one randomly had milk placed before the tea. He repeated the test 8 times. The lady was correct each time. Fisher found that she had a 1 in 70 chance of getting all 8 test correct, which is a statistically significant result.
11. Associative Hypothesis
An associative hypothesis predicts that two variables are linked but does not explore whether one variable directly impacts upon the other variable.
We commonly refer to this as “correlation does not mean causation”. Just because there are a lot of sick people in a hospital, it doesn’t mean that the hospital made the people sick. There is something going on there that’s causing the issue (sick people are flocking to the hospital).
So, in an associative hypothesis, you note correlation between an independent and dependent variable but do not make a prediction about how the two interact. You stop short of saying one thing causes another thing.
Associative Hypothesis Examples
- Sick people in hospital. You could conduct a study hypothesizing that hospitals have more sick people in them than other institutions in society. However, you don’t hypothesize that the hospitals caused the sickness.
- Lice make you healthy. In the Middle Ages, it was observed that sick people didn’t tend to have lice in their hair. The inaccurate conclusion was that lice was not only a sign of health, but that they made people healthy. In reality, there was an association here, but not causation. The fact was that lice were sensitive to body temperature and fled bodies that had fevers.
12. Causal Hypothesis
A causal hypothesis predicts that two variables are not only associated, but that changes in one variable will cause changes in another.
A causal hypothesis is harder to prove than an associative hypothesis because the cause needs to be definitively proven. This will often require repeating tests in controlled environments with the researchers making manipulations to the independent variable, or the use of control groups and placebo effects.
If we were to take the above example of lice in the hair of sick people, researchers would have to put lice in sick people’s hair and see if it made those people healthier. Researchers would likely observe that the lice would flee the hair, but the sickness would remain, leading to a finding of association but not causation.
Causal Hypothesis Examples
|Question||Causation Hypothesis||Correlation Hypothesis|
|Does marriage cause baldness among men?||Marriage causes stress which leads to hair loss.||Marriage occurs at an age when men naturally start balding.|
|What is the relationship between recreational drugs and psychosis?||Recreational drugs cause psychosis.||People with psychosis take drugs to self-medicate.|
|Do ice cream sales lead to increase drownings?||Ice cream sales cause increased drownings.||Ice cream sales peak during summer, when more people are swimming and therefore more drownings are occurring.|
13. Exact vs. Inexact Hypothesis
For brevity’s sake, I have paired these two hypotheses into the one point. The reality is that we’ve already seen both of these types of hypotheses at play already.
An exact hypothesis (also known as a point hypothesis) specifies a specific prediction whereas an inexact hypothesis assumes a range of possible values without giving an exact outcome. As Helwig argues:
“An “exact” hypothesis specifies the exact value(s) of the parameter(s) of interest, whereas an “inexact” hypothesis specifies a range of possible values for the parameter(s) of interest.”
Generally, a null hypothesis is an exact hypothesis whereas alternative, composite, directional, and non-directional hypotheses are all inexact.
This is introductory information that is basic and indeed quite simplified for absolute beginners. It’s worth doing further independent research to get deeper knowledge of research methods and how to conduct an effective research study. And if you’re in education studies, don’t miss out on my list of the best education studies dissertation ideas.