P-values are a common statistical measure used to determine the likelihood of a given result occurring by chance. A low P-value indicates a strong likelihood that the result is not due to chance, while a high P-value suggests that the result could have occurred by chance. However, there is a tendency for people to mis-interpret P-values, leading to incorrect conclusions.

One common mis-interpretation of P-values is the notion that a low P-value indicates a strong relationship between two variables. For example, imagine a study that examines the relationship between exercise and weight loss. The study finds that people who exercise regularly have a lower P-value for weight loss than those who do not exercise regularly. Some may interpret this to mean that exercise causes weight loss. However, this is not necessarily the case. A low P-value simply indicates that the relationship between exercise and weight loss is not likely to have occurred by chance. It does not prove that exercise causes weight loss.

Another mis-interpretation of P-values is the belief that a high P-value indicates a lack of relationship between two variables. For example, imagine a study that examines the relationship between pollution and asthma rates. The study finds that there is a high P-value for the relationship between pollution and asthma rates. Some may interpret this to mean that pollution does not cause asthma. However, this is not necessarily true. A high P-value simply indicates that the relationship between pollution and asthma rates could have occurred by chance. It does not prove that pollution does not cause asthma.

In both of these examples, the mis-interpretation of P-values can lead to incorrect conclusions. In the first example, people may assume that exercise causes weight loss, when in fact there could be other factors at play. In the second example, people may assume that pollution does not cause asthma, when in fact it may still be a contributing factor.

One way to avoid mis-interpreting P-values is to carefully interpret the results of a study. This means looking at all of the available evidence and considering all possible explanations for the results. Additionally, it is important to recognize that P-values are just one piece of the puzzle and should not be used as the sole basis for making conclusions about a relationship between two variables.