Recent reporting and analysis focus on the statistical concept known as the p-value and on why it is frequently misinterpreted in scientific papers. Across sources, the expert explanation emphasizes that a p-value is not the probability that a study’s result is true, nor is it a direct measure of effect size. Instead, it is commonly defined as a measure related to how surprising the observed data are under a specific statistical model, often involving the null hypothesis. The coverage also highlights that misunderstanding this number can lead to poor conclusions, particularly when p-values are treated as a straightforward indicator of “proof” or when results are overinterpreted relative to the threshold used in many fields. The sources describe broader consequences of reducing evidence to a single number, noting that such practices can shape how researchers, reviewers, and readers interpret findings. The articles aim to clarify the correct meaning and the limits of p-values to support more careful reading and interpretation of statistical results in research.