stat_samplesize: Calculate Required Sample Size
In evanverse: Utility Functions for Data Analysis and Visualization

stat_samplesize

R Documentation

Calculate Required Sample Size

Description

Compute the minimum sample size required to achieve a specified statistical power for detecting a given effect size. Supports multiple test types including t-tests, ANOVA, proportion tests, correlation tests, and chi-square tests.

Usage

stat_samplesize(
  power = 0.8,
  effect_size,
  test = c("t.test", "anova", "proportion", "correlation", "chisq"),
  type = c("two.sample", "one.sample", "paired"),
  alternative = c("two.sided", "less", "greater"),
  alpha = 0.05,
  k = NULL,
  df = NULL,
  plot = TRUE,
  plot_range = NULL,
  palette = "qual_vivid",
  verbose = TRUE
)

Arguments

`power`	Numeric. Target statistical power (probability of correctly rejecting the null hypothesis). Default: 0.8 (80%).
`effect_size`	Numeric. Effect size appropriate for the test: 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) Correlation coefficient r for correlation tests (small: 0.1, medium: 0.3, large: 0.5) Cohen's w for chi-square tests (small: 0.1, medium: 0.3, large: 0.5)
`test`	Character. Type of statistical test: "t.test" (default), "anova", "proportion", "correlation", or "chisq".
`type`	Character. For t-tests only: "two.sample" (default), "one.sample", or "paired".
`alternative`	Character. Direction of alternative hypothesis: "two.sided" (default), "less", or "greater".
`alpha`	Numeric. Significance level (Type I error rate). Default: 0.05.
`k`	Integer. Number of groups (required for ANOVA).
`df`	Integer. Degrees of freedom (required for chi-square tests).
`plot`	Logical. Generate a sample size curve plot? Default: TRUE.
`plot_range`	Numeric vector of length 2. Range of effect sizes for the curve. If NULL (default), automatically determined.
`palette`	Character. evanverse palette name for the plot. Default: "qual_vivid".
`verbose`	Logical. Print detailed diagnostic information? Default: TRUE.

Details

Sample size estimation is a critical step in research planning. This function calculates the minimum number of participants needed to achieve a specified statistical power (typically 0.8 or 80%) for detecting an effect of a given size.

The function uses the pwr package for all calculations, ensuring accurate results based on well-established statistical theory. Sample sizes are always rounded up to the nearest integer.

Value

An object of class stat_samplesize_result containing:

n: Required sample size (per group for t-tests and ANOVA)
n_total: Total sample size across all groups
power: Target statistical power
effect_size: Effect size used in the calculation
alpha: Significance level
test_type: Type of statistical test
plot: ggplot2 object showing the sample size curve (if plot = TRUE)
details: List with interpretation and recommendations

Sample Size Curve

When plot = TRUE, a sample size curve is generated showing how required sample size changes with effect size. The curve helps visualize:

The current required sample size (marked with a red point)
Reference lines for small, medium, and large effects
How detecting smaller effects requires larger samples

Important Notes

Sample sizes are calculated per group for t-tests and ANOVA
Consider adding 10-15% to account for potential dropout
Very small effect sizes may require impractically large samples

Examples

## Not run: 
# Example 1: Sample size for a two-sample t-test
result <- stat_samplesize(
  power = 0.8,
  effect_size = 0.5,
  test = "t.test",
  type = "two.sample"
)
print(result)
plot(result)

# Example 2: Sample size for ANOVA with 3 groups
stat_samplesize(
  power = 0.8,
  effect_size = 0.25,
  test = "anova",
  k = 3
)

# Example 3: Sample size for correlation test
stat_samplesize(
  power = 0.9,
  effect_size = 0.3,
  test = "correlation"
)

## End(Not run)

evanverse documentation built on March 10, 2026, 5:07 p.m.