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.
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
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.
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
See also
stat_power for calculating statistical power.
Examples
if (FALSE) { # \dontrun{
# 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"
)
} # }