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Calculate Required Sample Size

Source: R/stat.R
stat_n.Rd

Computes the minimum sample size needed to achieve a target statistical power for detecting a given effect size. Supports t-tests (two-sample, one-sample, paired), ANOVA, proportion tests, correlation tests, and chi-square tests.

Usage

stat_n(
  power = 0.8,
  effect_size,
  test = c("t_two", "t_one", "t_paired", "anova", "proportion", "correlation", "chisq"),
  alternative = c("two.sided", "less", "greater"),
  alpha = 0.05,
  k = NULL,
  df = NULL
)

Arguments

power

Numeric. Target statistical power. Default: 0.8.

effect_size

Numeric. Effect size appropriate for the chosen test (must be positive). Conventions:

  • Cohen's d for t-tests (small 0.2, medium 0.5, large 0.8)

  • Cohen's f for ANOVA (small 0.1, medium 0.25, large 0.4)

  • Cohen's h for proportion tests (small 0.2, medium 0.5, large 0.8)

  • Pearson r for correlation (small 0.1, medium 0.3, large 0.5)

  • Cohen's w for chi-square (small 0.1, medium 0.3, large 0.5)

test

Character. Test type: "t_two" (two-sample t-test, default), "t_one" (one-sample t-test), "t_paired" (paired t-test), "anova", "proportion", "correlation", or "chisq".

alternative

Character. Direction of the alternative hypothesis: "two.sided" (default), "less", or "greater". Ignored for "anova" and "chisq".

alpha

Numeric. Significance level (Type I error rate). Default: 0.05.

k

Integer \(\ge 2\). Number of groups. Required when test = "anova".

df

Integer \(\ge 1\). Degrees of freedom. Required when test = "chisq".

Value

An object of class "power_result" (invisibly) containing:

params

Named list of all input parameters

n

Required sample size, rounded up. Per group for "t_two" and "anova"; number of pairs for "t_paired"; total for all others.

computed

"n" — distinguishes from stat_power() results where computed = "power"

interpretation

Plain-text interpretation

recommendation

Practical recruitment advice

Use print(result) for a brief summary, summary(result) for full details, and plot(result) to display the sample size curve.

See also

stat_power for computing power given sample size.

Examples

if (FALSE) { # \dontrun{
# Two-sample t-test (default)
result <- stat_n(power = 0.8, effect_size = 0.5)
print(result)
summary(result)
plot(result)

# ANOVA with 4 groups, higher power target
stat_n(power = 0.9, effect_size = 0.25, test = "anova", k = 4)

# Correlation, one-sided
stat_n(power = 0.8, effect_size = 0.3, test = "correlation",
       alternative = "greater")
} # }