The Statistical Significance Filter in Studies

Studies based on statistics can exaggerate effect sizes due to the statistical significance filter. This filter causes a mathematical bias leading to overestimation of results. One example highlights the challenge of extracting accurate information from a small sample size, making it difficult to separate real effects from random chance. Statistical significance, often reliant on large differences, can render results misleading, as the confidence gained may be in noise rather than truth. Furthermore, in noisy studies, there is a significant risk that the actual effect might be contrary to the reported findings, even if deemed statistically significant.