KnowraEffect sizeLinked fromLinked fromThe 20 pages that link to Effect size, each with the reason it gives.All 20Related 20Publication biasRelated: Published effect sizes can be inflated when small or null results are missing.P-valueRelated: Unlike a p-value, effect size describes magnitude rather than compatibility with a null model.Meta-analysisRelated: Meta-analysis combines comparable effect-size estimates across studies.Statistical powerRelated: Larger effects are generally easier for a test to detect, increasing power.Analysis of varianceRelated: An ANOVA p-value does not show how large or practically meaningful an effect is.Statistical significanceRelated: A small effect can be significant with enough data, while a large estimate can remain uncertain.Multiple comparisons problemRelated: Adjusted significance alone does not show whether a finding is practically large.Null hypothesisRelated: A small p-value against the null does not reveal whether the estimated effect is practically large.ReplicationRelated: Comparing effect sizes reveals whether a repeated result matches the original beyond mere significance.Mindfulness-based stress reductionRelated: Effect sizes help interpret how large MBSR’s measured benefits are, beyond statistical significance.Statistical hypothesis testingRelated: A small p-value alone does not show whether the detected effect matters in magnitude.Type I and type II errorsRelated: Small effects are generally harder to detect, increasing the risk of type II error.Sample sizeRelated: Smaller effects require larger samples to distinguish them reliably from random variation.BiostatisticsRelated: Magnitude matters clinically even when a small effect achieves statistical significance.Quantitative dataRelated: Effect sizes show how substantial a pattern is beyond whether it is statistically detectable.Student's t-testRelated: A small p-value does not show whether the tested mean difference is practically substantial.Null hypothesis significance testingRelated: A significant result does not by itself show that the estimated effect is large.Sample size determinationRelated: Smaller target effects generally require larger samples to detect.Statistical hypothesis testRelated: Tests can detect small effects in large samples, so significance alone does not show magnitude.Eyeball theoremRelated: It distinguishes a visually noticeable effect from one that is substantively large.
KnowraEffect sizeLinked fromLinked fromThe 20 pages that link to Effect size, each with the reason it gives.All 20Related 20Publication biasRelated: Published effect sizes can be inflated when small or null results are missing.P-valueRelated: Unlike a p-value, effect size describes magnitude rather than compatibility with a null model.Meta-analysisRelated: Meta-analysis combines comparable effect-size estimates across studies.Statistical powerRelated: Larger effects are generally easier for a test to detect, increasing power.Analysis of varianceRelated: An ANOVA p-value does not show how large or practically meaningful an effect is.Statistical significanceRelated: A small effect can be significant with enough data, while a large estimate can remain uncertain.Multiple comparisons problemRelated: Adjusted significance alone does not show whether a finding is practically large.Null hypothesisRelated: A small p-value against the null does not reveal whether the estimated effect is practically large.ReplicationRelated: Comparing effect sizes reveals whether a repeated result matches the original beyond mere significance.Mindfulness-based stress reductionRelated: Effect sizes help interpret how large MBSR’s measured benefits are, beyond statistical significance.Statistical hypothesis testingRelated: A small p-value alone does not show whether the detected effect matters in magnitude.Type I and type II errorsRelated: Small effects are generally harder to detect, increasing the risk of type II error.Sample sizeRelated: Smaller effects require larger samples to distinguish them reliably from random variation.BiostatisticsRelated: Magnitude matters clinically even when a small effect achieves statistical significance.Quantitative dataRelated: Effect sizes show how substantial a pattern is beyond whether it is statistically detectable.Student's t-testRelated: A small p-value does not show whether the tested mean difference is practically substantial.Null hypothesis significance testingRelated: A significant result does not by itself show that the estimated effect is large.Sample size determinationRelated: Smaller target effects generally require larger samples to detect.Statistical hypothesis testRelated: Tests can detect small effects in large samples, so significance alone does not show magnitude.Eyeball theoremRelated: It distinguishes a visually noticeable effect from one that is substantively large.