What is the P-value?

It defines the probability of the result taking place from the sample space by chance. P-value varies from 0 to 100%. Note that a lower p-value is considered good as it implies that a result didn’t take place by chance.

The strength of hypothesis testing is indicated by the p-value. We develop hypotheses based on statistical models, and we use the p-value to assess the validity of the models. Using the T-test is one method of obtaining the p-value.

Example of P-value

Let’s understand the p-value in depth with the help of the scenario.

A company wants to test if its new marketing campaign increases brand awareness. They split their target audience into two groups, one exposed to the new campaign and the other not. After a campaign period, they measure brand awareness in both groups.

• The null hypothesis (H₀) states that the new campaign does not affect brand awareness. In other words, the observed difference between the two groups is just due to random chance.
• The alternative hypothesis (H₁) states that the new campaign does affect brand awareness.

P-value comes into play here. It represents the probability of obtaining the observed difference in brand awareness between the two groups, or a more extreme difference, assuming the null hypothesis is true.

• Lower p-value: This indicates a lower probability of observing the difference under the null hypothesis, meaning the observed difference is unlikely to be due to chance alone. This strengthens the evidence against the null hypothesis and supports the alternative hypothesis.
• Higher p-value: This indicates a higher probability of observing the difference under the null hypothesis, meaning the observed difference might be due to chance. This weakens the evidence against the null hypothesis and fails to support the alternative hypothesis.

In the marketing campaign example,

• if the p-value is very small (e.g., 0.01), it suggests the observed increase in brand awareness is unlikely to occur by chance. This provides strong evidence that the new campaign actually increased brand awareness.
• If the p-value is relatively large, the observed difference might be due to chance, weakening the evidence against the null hypothesis.

If the p-value is below the significance level(0.05 in most cases), you reject the null hypothesis and conclude that the new campaign had a significant effect on brand awareness.

Therefore, a lower p-value is better because it indicates stronger evidence against the null hypothesis in a statistical test stating that there is no significant difference between two groups or that there is no relationship between two variables.

In the realm of statistical analysis, the p-value stands as a pivotal metric, guiding researchers in drawing meaningful conclusions from their data. This article delves into the significance and computation of p-values in Python, focusing on the t-test, a fundamental statistical tool.