A hypothesis is a stepping stone to proving a theory. There are numerous types of hypotheses that can be employed when seeking to prove a theory. Additionally, there are many hypothesis examples that can help you form your own hypothesis.

Simply put, a hypothesis is an idea that can be tested based on the evidence available. A concept or statement must be tested to be proven credible. This serves as a starting point for further investigation to prove the hypothesis by applying the scientific method. However, there are multiple variables that can affect the results, and therefore the idea must be tested multiple times.

**Examples of Hypotheses**

- “Students who eat breakfast will perform better on a math exam than students who do not eat breakfast.”
- “Students who experience test anxiety prior to an English exam will get higher scores than students who do not experience test anxiety.”

## Intro to Hypothesis Testing in Statistics – Hypothesis Testing Statistics Problems & Examples

## How to write a hypothesis

Whether your hypothesis is formatted as an “if/then” statement or a declarative sentence, all hypotheses contain the same basic elements. Here are the key steps to writing a hypothesis:

## What is a hypothesis?

A hypothesis is an educated guess based on existing knowledge and observation. Hypotheses begin to form when a person notices something and asks a question about why it happens that way. The prediction of what the answer might be becomes the basis for a hypothesis that can then be tested and proved or disproved.

Hypotheses are usually written as “if/then” statements that relate a phenomenon to its potential cause. However, hypotheses can also be framed as simple declarative statements, such as, “Employees who use their own water bottles visit the office water cooler most often.”

Here is a list of seven common types of hypotheses:

All hypotheses mention at least two variables:

## Hypothesis examples

Here are examples of the seven common types of hypotheses noted above:

**Simple hypothesis**

Employees who bring their own lunch spend less money throughout the day.

A simple hypothesis addresses the relationship between two variables. In this case, the independent variable is whether an employee brings their own lunch, and the dependent variable is how much money they spend throughout the day.

A simple hypothesis does not include other variables, such as whether these employees spend money on their commute to work, but additional variables can be considered during the research step to refine a simple hypothesis and make it as specific as possible.

**Complex hypothesis**

If I give my employees a holiday bonus and paid time off, then they will work harder during the year and increase their morale.

A complex hypothesis examines the relationship between more than two variables. Here, there are two independent variables: giving a holiday bonus and giving paid time off.

There are also two dependent variables: how hard the employees work and an increase in office morale. A complex hypothesis is most useful in situations that consider a number of factors that have the potential to affect each other.

**Null hypothesis**

The number of working hours in a day does not affect employee morale.

A null hypothesis predicts no relationship between an independent and dependent variable and might be created once an original hypothesis between those variables has been disproven. Writing null hypotheses is a great way to consider all possible outcomes of an experiment based on the prediction the original hypothesis made.

**Alternative hypothesis**

Employees will be more productive if they are given one break every two hours, as opposed to one break every four hours.

An alternative hypothesis is created to disprove a null hypothesis after the original hypothesis has already been found to be incorrect. In this example, the original hypothesis might have been “If an employee is given one break every four hours, they will be more productive,” and the null hypothesis might have been “The number of breaks does not have an effect on employee productivity.”

**Logical hypothesis**

If an employee is late to work, then traffic must be heavy.

A logical hypothesis is not based on evidence or proven fact but comes from reaching a logical conclusion from observation. There might be other explanations for why an employee would be late to work, and it is not clear whether the employee drove to work or if they took a route with heavy traffic. However, a logical hypothesis can be effective when trying to reach a conclusion and adapt to a problem quickly.

**Statistical hypothesis**

70% of employees in the office prefer to use a Mac over a PC.

A statistical hypothesis considers a measurable data prediction derived from a set of variables. In this case, the hypothesis makes a prediction about 70% of the employees in an office, though testing might discover that 100% prefer a Mac or that 80% prefer a PC. Writing the initial hypothesis to consider a certain portion of the bigger population provides room for revision and further experimentation based on testing results.

**Empirical hypothesis**

Employees will complete their work faster if the temperature in an office is 70 degrees.

An empirical hypothesis is based on experiments or observations and lends itself to be proven and supported or to be disproven to form an alternative hypothesis.

With this example, the opportunity for proving the hypothesis could come from changing the temperature of the office after evaluating how quickly employees complete their work when the office is below 70 degrees. An alternative hypothesis for this example could read: “Employees complete their work faster if the temperature in an office is 68 degrees.”

## FAQ

**What is an example of a hypothesis in research?**

**suppose a doctor believes that a new drug is able to reduce blood pressure in obese patients**. To test this, he may measure the blood pressure of 40 patients before and after using the new drug for one month.

**What is a real life example of a hypothesis?**

**ones which give probabilities to potential observations**. The contrast here is with complex hypotheses, also known as models, which are sets of simple hypotheses such that knowing that some member of the set is true (but not which) is insufficient to specify probabilities of data points.

**What is a simple hypothesis?**

**ones which give probabilities to potential observations**. The contrast here is with complex hypotheses, also known as models, which are sets of simple hypotheses such that knowing that some member of the set is true (but not which) is insufficient to specify probabilities of data points.