What is a p-value?

A p-value, short for probability value, is a statistical measurement used in hypothesis testing. It tells you the probability of observing the results you obtained in your study, assuming the null hypothesis is true.

The null hypothesis, typically denoted by H₀, represents the default assumption that there is no effect or difference between the groups being compared.

Here's a breakdown of the key points:

• Probability: The p-value is a number between 0 and 1. The closer the p-value is to 0, the less likely it is that your observed results could have occurred by chance if the null hypothesis is true.
• Statistical significance: A commonly used threshold for statistical significance is 0.05. This means that if the p-value is less than 0.05, we can reject the null hypothesis and conclude that there is evidence to support the alternative hypothesis (often denoted by H₁). However, it is important to note that the choice of significance level is a matter of scientific judgement and depends on the specific context of the study.

What do you use the p-value for?

We use p-values to help us draw conclusions from our data in a scientific way. They help us:

• Assess the strength of evidence against the null hypothesis. A lower p-value indicates stronger evidence against the null hypothesis.
• Make decisions about rejecting or failing to reject the null hypothesis. However, it is crucial to remember that a p-value does not tell us the probability that the alternative hypothesis is true, nor does it provide the strength of the effect. It simply tells us the likelihood of observing such an extreme result, assuming no effect exists.

Therefore, p-values should be interpreted cautiously and in conjunction with other factors such as the effect size, study design, and prior research in the field.

What is a commonly used cut-off for the p-value?

In hypothesis testing, a commonly used cut-off for statistical significance is 0.05. This means if the p-value is less than 0.05, we can reject the null hypothesis and conclude evidence against it. However, other values like 0.01 or even 0.10 can be used depending on the context and desired level of stringency.