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.
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.
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
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.
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
if (FALSE) { # \dontrun{
# 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"
)
} # }