How can you explain the meaning of a p-value to a non-expert audience?

2 Hypothesis Testing

In hypothesis testing, the p-value helps you decide whether to reject the null hypothesis. The null hypothesis typically states that there is no effect or difference, while the alternative hypothesis states that there is. If you have a low p-value, it suggests that your data are inconsistent with the null hypothesis and that you may have found something significant. For instance, if you're testing a new drug and find a p-value of 0.01, it suggests there's only a 1% chance that the observed improvement in patients is due to random variation rather than the drug's effectiveness.

3 Misconceptions Cleared

There are common misconceptions about p-values that can lead to confusion. A p-value does not tell you the probability that the null hypothesis is true or false. It also doesn't measure the size of an effect or the importance of a result. Instead, it simply gives you a measure of the strength of the evidence against the null hypothesis. Remember, a low p-value indicates that what you observed is quite unlikely to occur if the null hypothesis were true, but it doesn't prove the alternative hypothesis.

4 Significance Levels

A significance level, often denoted as alpha, is a threshold you set before your study to decide when to reject the null hypothesis. Commonly set at 0.05, this level means you would expect to be wrong about rejecting the null hypothesis 5% of the time if it were actually true. If your p-value is below this threshold, you reject the null hypothesis in favor of the alternative. It's like setting a strict rule for how surprised you need to be by your data before you call it significant.

5 Context Matters

It's important to consider the context when interpreting p-values. They should not be used in isolation to make decisions or draw conclusions. Other factors like the size of the effect, the quality of the data, and the design of the study are crucial for proper interpretation. A very low p-value does not necessarily mean a finding is practically significant or that it will have a big impact. It's just one piece of the puzzle that needs to be combined with other evidence to form a complete picture.

6 Beyond P-Values

While p-values are widely used, they are not the only method for assessing the evidence in your data. Confidence intervals, Bayesian methods, and other statistical measures can provide additional insights or offer alternative ways of thinking about your results. It's essential to use the right tools for your specific questions and to understand the limitations and assumptions behind each method. By doing so, you can make more informed decisions and convey your findings more accurately.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?