P Values Explained in 1 Minute WILL CHANGE Everything!

The p-value, or probability value, is a statistical measure used to determine the likelihood of observing data at least as extreme as the observed results, assuming the null hypothesis is true. It is a key component in hypothesis testing, helping researchers decide whether to reject the null hypothesis[1][3][5]. Ronald A. Fisher popularized the concept of the p-value in the 1930s. Fisher introduced it as a measure of the strength of evidence against the null hypothesis, suggesting that a p-value less than 0.05 indicated statistically significant results. However, this threshold is not an absolute rule. The importance of the p-value lies in its ability to quantify the evidence against the null hypothesis. A low p-value suggests that the observed data is unlikely under the null hypothesis, potentially favoring an alternative hypothesis. This makes it a critical tool in determining the statistical significance of research findings. However, p-values have been criticized for being misinterpreted and overemphasized in research. They do not measure the probability that the null hypothesis is true or the size of an effect, and reliance solely on p-values can lead to misleading conclusions