What is Null Hypothesis?

Null hypothesis is a type of hypothesis that states the opposite of the alternative hypothesis to be checked, i.e., that no significant statistical relationship exists between the two variables and that the observations are all based on chance. A null hypothesis can be rejected or confirmed during a statistical experiment. In statistical theory, it serves as the default assumption that there is no effect, no difference, or no correlation in the population being studied.

 

Why is the Null Hypothesis used?

The null hypothesis is used to provide a default baseline for scientific and statistical research. It allows researchers to test data systematically. Instead of trying to prove that an effect exists outright, researchers attempt to disprove the null hypothesis. If the data shows that the results are highly unlikely to occur under the assumption that the null hypothesis is true, researchers have valid, objective grounds to reject it.

 

What is the purpose of the Null Hypothesis?

The purpose of the null hypothesis is to provide a baseline for statistical testing. Researchers and analysts use it to ensure that their findings are not simply the result of random variations in the data sample. By testing data against this baseline, they can objectively measure if their observed results represent a genuine effect or relationship.

 

How does the Null Hypothesis differ from the Alternative Hypothesis?

• The null hypothesis assumes that there is no effect, no difference, or no relationship between the variables being studied.
• The alternative hypothesis claims the exact opposite: that a measurable effect, difference, or relationship does exist.

A statistical test evaluates the available data to determine which of these two statements is supported by the evidence.

 

What does it mean to "reject" the Null Hypothesis?

Rejecting the null hypothesis means that the statistical test of the data resulted in an outcome that is highly unlikely to occur by random chance alone. This rejection indicates that the data provides sufficient evidence to support the alternative hypothesis. Conversely, failing to reject the null hypothesis means there is not enough evidence to prove that a relationship or effect exists.