Tag
p-values
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What counts as a family when you correct for multiple comparisons?
How to decide which tests share one familywise error correction, with a simulation of per-outcome versus pooled Holm corrections.
Read the answer →Is stepwise regression a good way to choose predictors?
Automatic stepwise selection finds patterns in pure noise and leaves p-values and R-squared too optimistic. Here is why, and what to do instead.
Read the answer →Bayesian vs frequentist statistics in plain English
Frequentists treat probability as long-run frequency; Bayesians treat it as degree of belief. Here is what that changes, with one example done both ways.
Read the answer →False positive risk: how likely is a significant finding to be wrong?
Why low power raises the share of significant findings that are false, inflates the real effects that get through, and what one researcher can do.
Read the answer →What should you do after rejecting the null hypothesis?
A significant result is a starting point, not a conclusion. Estimate the effect, check that it is robust, and replicate before you treat it as established.
Read the answer →How to interpret the F statistic and p value in a one-way ANOVA
What the F ratio in an ANOVA table measures, how it maps onto the p value and the critical F, and what a significant F does and does not tell you.
Read the answer →Planned contrasts vs post hoc tests: why do they give different p-values?
Why one comparison gets different p-values as a planned contrast, a Tukey test or a Bonferroni test, and when to use each.
Read the answer →Bonferroni correction: when should you use it, and what are the alternatives?
What a Bonferroni correction protects against, why it costs so much power, and how Holm and Benjamini-Hochberg compare.
Read the answer →Why does almost everything become statistically significant with a large sample?
Large samples do not bias a test toward rejecting a true null; they detect tiny real differences. How to tell significant from important.
Read the answer →Is a non-significant result from a large study evidence for the null?
When failing to reject the null hypothesis tells you something, why the p value alone cannot show it, and how equivalence tests make the case.
Read the answer →One-tailed vs two-tailed tests: why not just test the direction the data point to?
Why a two-tailed test is the default, why choosing the tail after seeing the data doubles your false-positive rate, and when a one-tailed test is legitimate.
Read the answer →How do you interpret regression output in R?
A line-by-line guide to R's linear model summary: residuals, estimates, standard errors, t values, p-values, R-squared and the F test.
Read the answer →What do p values and t values actually mean?
A t value is an effect measured in standard errors; the p value turns it into a tail probability under the null. What both mean, in plain language.
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