What is p-value?

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Introduction

In statistics, the p-value is a measure of the evidence against a null hypothesis. It helps us determine the statistical significance of our findings. Understanding p-values is crucial for interpreting research results and making informed decisions.

Concepts

The p-value represents the probability of obtaining results as extreme as the observed data, assuming the null hypothesis is true. A p-value of less than a predetermined significance level (often 0.05) indicates that the data provides strong evidence against the null hypothesis.

Example Questions

What is the null hypothesis?

How is the p-value calculated?

What does a p-value of 0.01 signify?

Answers to the Example Questions

The null hypothesis is a statement of no effect or no difference between groups or variables.

The p-value is calculated by determining the probability of obtaining results as extreme or more extreme than the observed data, assuming the null hypothesis is true.

A p-value of 0.01 signifies that there is a 1% chance of obtaining results as extreme as the observed data, assuming the null hypothesis is true.

Exercises

A study reports a p-value of 0.02. What does this suggest?

If the p-value is greater than the significance level, what does it imply about the null hypothesis?

Answers to the Exercises

A p-value of 0.02 suggests that there is a 2% chance of obtaining results as extreme as the observed data, assuming the null hypothesis is true. This provides moderate evidence against the null hypothesis.

If the p-value is greater than the significance level, it implies that the data does not provide strong evidence against the null hypothesis. However, it does not necessarily prove the null hypothesis to be true.

By understanding p-values, researchers can make informed decisions about the significance of their findings and draw meaningful conclusions from statistical analyses.