The p-value is one of the most essential concepts in statistical analysis and hypothesis testing. If you’ve ever struggled to understand what a p-value really means, this simple and visual guide is made just for you! In this article, we’ll explain what a p-value is, how to interpret it, and how it plays a crucial role in statistical decision-making—all in plain English.

A p-value is used to determine the probability of observing a result (or something more extreme) assuming the null hypothesis is true. The null hypothesis typically claims that there is no effect or no relationship between two variables or groups.

During hypothesis testing, researchers calculate the p-value based on the data they’ve collected. A small p-value (commonly less than 0.05) suggests that the observed results are unlikely to have occurred by chance, and the null hypothesis is rejected. On the other hand, a large p-value (commonly greater than 0.05) suggests that the results could reasonably have happened by chance, so the null hypothesis is not rejected.

Put simply, a p-value tells you how likely it is that your results are due to random chance rather than a real effect. A lower p-value means your results are more statistically significant, and less likely to be due to luck. A higher p-value indicates your results are less meaningful and may have occurred by chance. Thus, the p-value is a statistical indicator that helps researchers determine whether the findings of their study are meaningful or likely the result of random variation. By interpreting the p-value, researchers can draw conclusions and make informed decisions based on their data.

Imagine you’re testing whether a new drug is effective in treating a certain disease. Your null hypothesis (H₀) might be that the drug has no effect, while your alternative hypothesis (H₁) is that the drug is effective. After collecting data from a sample of patients, you calculate the p-value:

If the p-value is less than 0.05, it suggests that the observed results are unlikely to occur if the null hypothesis were true, so you reject the null hypothesis. This implies there is evidence that the drug is effective. If the p-value is greater than 0.05, it indicates the results could reasonably occur by chance, and you do not reject the null hypothesis. This means there’s not enough evidence to prove the drug is effective.