---
title: "t Tests"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{t Tests}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(ggpower)
```

t tests cover mean differences, matched pairs, point-biserial correlations,
regression slopes, and generic noncentrality.

## One sample

$$d = \frac{\mu_1 - \mu_0}{\sigma}, \quad \delta = d\sqrt{n}$$

```{r one}
power_compute("t_one_sample", "a_priori", d = 0.625, alpha = 0.05,
              power = 0.95, tails = "one")
```

## Two independent means

```{r two}
power_compute("t_two_sample", "a_priori", d = 0.5, alpha = 0.05,
              power = 0.8, tails = "two", allocation_ratio = 1)
```

## Matched pairs

```{r paired}
power_compute("t_paired", "post_hoc", d = 0.42, n = 50, alpha = 0.05)
```

## Point-biserial correlation

```{r pb}
power_compute("t_point_biserial", "a_priori", rho = 0.3, alpha = 0.05, power = 0.8)
```

## Linear regression slope

```{r slope}
power_compute("t_linear_regression", "post_hoc", slope_h1 = -0.0667,
              slope_h0 = 0, sd_x = 7.5, sd_y = 4, n = 100)
```

## Two-group slope difference

```{r slope2}
power_compute("t_linear_regression_two_groups", "a_priori", delta_slope = 0.1,
              sd_x1 = 1, sd_x2 = 1, residual_sd = 1, alpha = 0.05, power = 0.8)
```

## Generic t (direct NCP)

No `a_priori` mode — supply NCP and df directly.

```{r generic}
power_compute("t_generic", "post_hoc", ncp = 3, df = 29, alpha = 0.05, tails = "two")
```

## Related

- [Analysis modes](analysis-modes.html)
- [Workspace test families](workspace-test-families.html)