[The p-value of a test is not the probability that the null hypothesis is true or false].

One of the most common errors made by physicians in all developed countries is to say that the p-value of a test is the probability that the null hypothesis considered in the test is true or false. Eighty percent of those polled in many surveys make this mistake. The p-value of a test is the probability of obtaining a result like the one obtained in the investigation if the null hypothesis is true.

[Statistical power in medical research. What position should be taken when research results are not significant?].

The original idea of rejecting studies with low power and authorising them if their power is sufficiently high is reasonable and even an obligation, although in practice this reasoning is heavily constrained by the fact that the power of a study depends on several factors, rather than a single one. Furthermore, there is no threshold separating 'high' power values from 'low' power values'. However, if the result is very significant, considering how powerful it was it makes little sense after the study has been carried out.

[The enormous difference between not rejecting a null hypothesis and stating that it is true].

Assuming that a hypothesis is true because insufficient evidence has been found to reject it is a very common error when interpreting the p-value of a test in biomedical research. For example, a value of p = 0.28 obviously does not mean the null hypothesis should be ruled out, but if we understand what it means (which is not a mathematical issue, but instead a purely logical one) that it is equally obvious that it cannot be stated that it is true.

[The p-value of the test is not a 'mathematical index', it is simply a relative frequency].

Leading scientific journals in fields such as medicine, biology and sociology repeatedly publish articles and editorials claiming that a large percentage of doctors do not understand the basics of statistical analysis, which increases the risk of errors in interpreting data, makes them more vulnerable to misinformation and reduces the effectiveness of research. This problem extends throughout their careers and is largely due to the poor training they receive in statistics - a problem that is common in developed countries. As stated by H.

[The dramatic loss of statistical power when dichotomising continuous variables].

A very common practice in medical research, during the process of data analysis, is to dichotomise numerical variables in two groups. This leads to the loss of very useful information that can undermine the effectiveness of the research. Several examples are used to show how the dichotomisation of numerical variables can lead to a loss of statistical power in studies.