Statistical Communication AI Prompts

Write the statistical methods section of a research paper or technical report for this analysis. Study design: {{study_design}} Data: {{data_description}} Primary analysis: {{primary_analysis}} Secondary analyses: {{secondary_analyses}} Software: {{software}} Journal / reporting standard: {{reporting_standard}} (CONSORT, STROBE, ARRIVE, APA, etc.) 1. Participants and data: - Sample description: how were participants/observations selected? - Inclusion and exclusion criteria - Sample size and statistical rationale (brief reference to power analysis) 2. Statistical methods: - Primary outcome: describe the variable and its measurement level - Descriptive statistics: state how continuous variables are summarized (mean ± SD, or median [IQR] if non-normal); categorical variables as count (%) - Primary analysis: name the test, state the null hypothesis, and specify the significance threshold - Secondary analyses: list any planned comparisons or subgroup analyses - Multiple testing: if multiple tests, specify the correction method - Handling of missing data: complete case, multiple imputation (state the model), or other 3. Model assumptions: - State which assumptions were checked and how - State what action was taken if assumptions were violated 4. Software and packages: - 'All analyses were conducted in R version {{version}} (R Core Team, {{year}}) using the packages {{list}}' - or 'Python version X.X using statsmodels X.X, scipy X.X' 5. Reporting standards to reference: - CONSORT (for RCTs): report CONSORT flow diagram - STROBE (for observational studies): 22-item checklist - PRISMA (for systematic reviews): 27-item checklist - ARRIVE 2.0 (for animal research): 21 items 6. Preregistration: - If applicable: 'The primary outcome, hypotheses, and analysis plan were pre-registered at {{registry}} (registration number: {{number}})' Return: complete statistical methods section text suitable for inclusion in a research paper, formatted according to the specified reporting standard.

Interpret and communicate these statistical results for a non-technical audience. Statistical results: {{results}} Audience: {{audience}} (business stakeholders, clinical team, policymakers, general public) Context: {{context}} 1. Lead with the scientific conclusion, not the statistic: - Start with what it means for people and decisions, not the p-value - Wrong: 'The t-test yielded t(48) = 2.3, p = 0.026' - Right: 'Patients receiving the new treatment recovered an average of 3 days faster than controls' 2. Effect size before statistical significance: - Report the magnitude of the effect, not just whether it is statistically significant - 'The intervention increased sales by 12% (95% CI: 7% to 17%)' - A large sample can produce a statistically significant but practically meaningless effect - A small sample can fail to detect a large and important effect 3. Confidence intervals over p-values: - Report 95% CIs alongside point estimates - CI communicates uncertainty: a wide interval means we are less sure about the true effect - 'We are 95% confident the true effect is between 7% and 17%' - Never say 'the probability that the true value is in this interval is 95%' (frequentist CI does not have this interpretation) 4. Practical significance: - Is the effect large enough to matter for the decision at hand? - Provide a concrete translation: 'An 8% reduction in churn would save approximately $2M annually' - Benchmark against a meaningful threshold, not just 'statistically significant' 5. What statistical significance does and does NOT mean: - It means: if the null hypothesis were true, we would rarely see results this extreme by chance - It does NOT mean: the effect is large, important, replicable, or clinically meaningful - p > 0.05 does NOT mean the null hypothesis is true 6. Uncertainty and limitations: - What assumptions could be violated? - What alternative explanations cannot be ruled out? - How would the interpretation change if the sample were different? Return: plain-language interpretation of each result, effect size with CI, practical significance assessment, and caveats.