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Power and sample size

Before you conduct your experiment, determine the sample size needed to detect meaningful effects without wasting resources. Do you intend to perform tests of means, variances, proportions, or correlations? Do you plan to fit a one-way, two-way, or repeated-measures ANOVA model? Do you want to fit a Cox proportional-hazards model or compare survivor functions using a log-rank test or exponential regression? Use Stata's power commands or interactive Control Panel to compute power and sample size, create customized tables, and automatically graph the relationships between power, sample size, and effect size for your planned study. And much more.

Classic comparisons

  • Means (t-tests)
  • Proportions
  • Variances
  • Correlations
  • McNemar's test

ANOVA

  • One-way models
  • Two-way models
  • Repeated-measures models

Contingency tables New

  • Cochran—Mantel—Haenszel test
  • Matched case—control studies
  • Cochran—Armitage trend test

Survival analysis Updated

  • Log-rank test of survival functions
  • Cox proportional hazards model
  • Exponential regression

Solve for

  • Power
  • Sample size
  • Effect size (minimum detectable effect)

Designs

  • Matched case–control studies
  • Cohort studies
  • Cross-sectional studies
  • Balanced
  • Unbalanced

Specify lists of

  • Alpha values
  • Power levels
  • Beta values
  • Sample sizes
  • Ratios of sample sizes
  • And more

Multiple scenarios using

  • All combinations of values
  • Parallel lists of values

Control panel interface

  • Select analysis type from descriptive summaries
  • Specify list of power, sample size, or other levels
  • Automated tables and graphs
  • Customize tables and graphs
  • Command interface too (of course)

Tables

  • Automated
  • Customized

Graphs

  • Automated
  • Customized
  • Simple or comparative

Save results to dataset

Add your own methods

Additional resources


See New in Stata 14 for more about what was added in Stata 14.

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