stat_power: Calculate Statistical Power

In evanverse: Utility Functions for Data Analysis and Visualization

Calculate Statistical Power

Description

Compute the statistical power (probability of correctly rejecting the null hypothesis) for a given sample size, effect size, and significance level. Supports multiple test types including t-tests, ANOVA, proportion tests, correlation tests, and chi-square tests.

Usage

stat_power(
  n,
  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

n	Integer. Sample size. Interpretation depends on test type: t-tests and ANOVA: Sample size per group Proportion test: Total sample size (one-sample test) Correlation: Total number of paired observations Chi-square: Total sample size
effect_size	Numeric. Effect size appropriate for the test (must be positive): 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). Use absolute value; power is the same for positive and negative correlations. 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". Note: Only applicable to t-tests, proportion tests, and correlation tests. Ignored for ANOVA and chi-square tests (which are inherently non-directional).
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 power curve plot? Default: TRUE.
plot_range	Numeric vector of length 2. Range of sample sizes for the power 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

Statistical power is the probability of correctly rejecting the null hypothesis when it is false (i.e., detecting a true effect). Conventionally, a power of 0.8 (80%) is considered adequate, though higher power (0.9 or 0.95) may be desirable in some contexts.

The function uses the pwr package for all power calculations, ensuring accurate results based on well-established statistical theory.

Value

An object of class stat_power_result containing:

power

The calculated statistical power (probability of detecting the effect)

n

Sample size used in the calculation

effect_size

Effect size used in the calculation

alpha

Significance level

test_type

Type of statistical test

test_subtype

Subtype for t-tests (e.g., "two.sample", "one.sample", "paired"); NULL for other tests

alternative

Direction of alternative hypothesis used in the test

k

Number of groups (for ANOVA); NULL for other tests

df

Degrees of freedom (for chi-square tests); NULL for other tests

plot

ggplot2 object showing the power curve (if plot = TRUE); NULL otherwise

pwr_object

Raw result object from the pwr package function

details

List with interpretation and recommendation text

timestamp

POSIXct timestamp of when the calculation was performed

Test-Specific Notes

Proportion Test: Uses pwr.p.test, which is for one-sample proportion tests (testing a single proportion against a hypothesized value). For two-sample proportion tests, consider using specialized tools or packages. The effect_size parameter uses Cohen's h, which quantifies the difference between two proportions. In one-sample settings, Cohen's h is computed from the observed proportion p and the null hypothesis proportion p0.

Power Curve

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

• The current power level (marked with a red point)

• The conventional 0.8 power threshold (red dashed line)

• How increasing sample size affects power

Examples

## Not run: 
# Example 1: Power for a two-sample t-test
result <- stat_power(
  n = 30,
  effect_size = 0.5,
  test = "t.test",
  type = "two.sample"
)
print(result)
plot(result)

# Example 2: Power for ANOVA with 3 groups
stat_power(
  n = 25,
  effect_size = 0.25,
  test = "anova",
  k = 3
)

# Example 3: Power for correlation test
stat_power(
  n = 50,
  effect_size = 0.3,
  test = "correlation"
)

## End(Not run)

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