Academic Significance Calculator | Research P-values

Modern academic publishing standards for p-value reporting include several key practices: 1) Report exact p-values (e.g., p = .032) rather than just p < .05, except for very small values which can be reported as p < .001. 2) Include appropriate test statistics and degrees of freedom, such as t(34) = 2.54 for a t-test with 34 degrees of freedom. 3) Always pair p-values with effect sizes (Cohen's d, η², r, etc.) to indicate practical significance. 4) Provide confidence intervals for main effects to show precision of estimates. 5) Round p-values to 2 or 3 decimal places in most fields. 6) Avoid terms like "highly significant" or "marginally significant"; let readers interpret the values. 7) For multiple comparisons, clearly state any corrections applied (e.g., Bonferroni, False Discovery Rate). 8) In tables, use asterisks consistently (typically * p < .05, ** p < .01, *** p < .001) with explanations in a note. 9) For non-significant results, still report exact p-values rather than just "n.s." 10) Follow specific journal guidelines, which may have unique requirements for statistical reporting. These practices promote transparency and reproducibility while helping readers properly interpret the results.

Different research designs require specific effect size measures to complement p-values: 1) For t-tests: Cohen's d (standardized mean difference) is most common. Small effect: d = 0.2, medium: d = 0.5, large: d = 0.8. 2) For ANOVAs: Partial eta squared (η²) for factorial designs; report for each main effect and interaction. Small effect: η² = 0.01, medium: η² = 0.06, large: η² = 0.14. 3) For chi-square tests: Cramer's V for contingency tables beyond 2×2; phi (φ) coefficient for 2×2 tables. Small effect: V = 0.1, medium: V = 0.3, large: V = 0.5. 4) For correlations: Pearson's r is itself an effect size. Small effect: r = 0.1, medium: r = 0.3, large: r = 0.5. 5) For regression: R² (proportion of variance explained) for overall model; standardized β coefficients for individual predictors. 6) For non-parametric tests: Consider rank biserial correlation, Cliff's delta, or appropriate equivalents. Always report effect sizes with confidence intervals when possible to indicate precision. Many journals now require effect sizes for publication, and some fields prioritize standardized measures (like Cohen's d) while others prefer unstandardized measures (like mean differences with units) for better interpretability. The Academic Significance Calculator provides appropriate effect size calculations for each test type it supports.

Non-significant p-values require nuanced interpretation in academic research: 1) Avoid concluding "there is no effect" – non-significance means insufficient evidence to reject the null hypothesis, not proof that the null is true. 2) Consider statistical power – small samples may fail to detect real effects. Report power analyses or calculate post-hoc power to contextualize non-significant results. 3) Examine confidence intervals – wide intervals that include both meaningful effects and zero indicate inconclusive results rather than "no effect." 4) Report exact p-values even when non-significant (p = .078 provides different information than p = .412). 5) Consider "equivalence testing" to positively establish the absence of meaningful effects. 6) Evaluate practical significance – even if statistically non-significant, is the observed effect size meaningful in your field? 7) Discuss potential reasons for non-significance: inadequate sample size, high variability, improper measures, theoretical misconceptions, or genuine absence of effect. 8) Avoid "p-hacking" by trying alternative analyses until finding significance. 9) Consider Bayesian alternatives that can provide evidence for the null hypothesis. 10) Remember that non-significant findings are valuable contributions to scientific knowledge and should be published to address publication bias. The "file drawer problem" occurs when only significant results are published, distorting the scientific record.