What are the dangers of misinterpreting p-values in scientific research?

Misinterpreting p-values in scientific research can lead to significant and far-reaching consequences. P-values are often misunderstood, leading to incorrect conclusions about research findings. Key dangers include: From my experience, its worth paying attention to these points: False Positives (Type I Errors) or Misinterpreting a low p-value as proof of a hypothesis can result in accepting non-existent effects. others points are: Neglecting effect size and practical significance, over emphasis on arbitrary thresholds, Ignoring the role of sample size, misunderstanding the null hypothesis, Ignoring multiple comparisons and reproducibility crisis

Other common misconceptions about p-values include believing they measure the probability that the alternative hypothesis is true or that they can be interpreted without considering the study context and sample size. Some think a p-value close to 0.05 is less reliable, not recognizing the arbitrary nature of this threshold. Others mistakenly assume p-values indicate the probability of a Type I error in a specific experiment or that they alone can determine scientific truth. This overemphasis on p-values often leads to neglecting other important aspects like confidence intervals, effect sizes, and practical significance.

It's crucial to understand that a p-value merely indicates how well the data align with the null hypothesis, assuming it is true. Additionally, the belief that a p-value predicts the likelihood of replicating results is another significant error. A p-value is derived from the current dataset and does not account for variability in future studies. This misunderstanding can result in misplaced confidence in the reproducibility of results. Proper interpretation of p-values requires recognizing their limitations and understanding that they do not provide direct probabilities about hypotheses or replication.

Misinterpreting p-values in scientific research can have significant real-world impacts. Overestimating the significance of findings can lead to false positives, where ineffective or harmful interventions are pursued. Conversely, underestimating significance may result in disregarding beneficial discoveries. These errors can misguide policy decisions, waste resources, and erode public trust in scientific findings. Accurate interpretation of p-values is essential for reliable conclusions, ensuring that subsequent research, clinical practices, and policy implementations are based on robust and valid evidence, ultimately advancing scientific progress and societal well-being.