# Understanding p-value

A p-value is a statistical measure that helps scientists and researchers understand the likelihood that a result occurred by chance. It is used to evaluate the strength of evidence in a scientific study or experiment.

In order to understand the concept of a p-value, it’s helpful to first understand the concept of hypothesis testing. Hypothesis testing is a statistical method used to determine whether a hypothesis (a statement or claim about a particular phenomenon) is true or false. When conducting a hypothesis test, there are two possible outcomes: either the hypothesis is true, or it is false.

In order to determine the likelihood that a hypothesis is true, scientists and researchers use statistical tests to compare their data to a certain “null hypothesis,” which is a hypothesis that states that there is no relationship between the variables being studied. For example, if a researcher is studying the effect of a new drug on blood pressure, the null hypothesis might be that the drug has no effect on blood pressure.

The p-value is a measure of how likely it is that the results of a study or experiment occurred by chance, given the null hypothesis. In other words, it’s a measure of how likely it is that the observed results occurred simply by random chance, rather than due to a real effect of the drug or other factor being studied.

The p-value is usually expressed as a decimal number between 0 and 1. A p-value of 0 means that it is impossible for the results of the study to have occurred by chance, given the null hypothesis. A p-value of 1 means that it is certain that the results occurred by chance. A p-value of 0.05, for example, means that there is a 5% chance that the results occurred by chance.