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35 changes: 35 additions & 0 deletions modules/Statistics/Statistics.Rmd
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Expand Up @@ -736,5 +736,40 @@ knitr::include_graphics(here::here("images/the-end-g23b994289_1280.jpg"))

Image by <a href="https://pixabay.com/users/geralt-9301/?utm_source=link-attribution&amp;utm_medium=referral&amp;utm_campaign=image&amp;utm_content=812226">Gerd Altmann</a> from <a href="https://pixabay.com//?utm_source=link-attribution&amp;utm_medium=referral&amp;utm_campaign=image&amp;utm_content=812226">Pixabay</a>

# Extra Slides

## Wilcoxon Test

The Wilcoxon test is a good alternative to the t-test when the normal distribution of the differences between paired individuals cannot be assumed.

`wilcox.test(x, y, ..)`

- Like t-test, provide one or two vectors (x, y)
- Choose from `alternative = c("two.sided", "less", "greater")`
- Use `paired = TRUE` for paired values (e.g., before and after)

## Shapiro Test

Can tell you if a vector is normally distributed.

`shapiro.test(x)`

The smaller the p-value, the more likely the data violates normality assumptions.

## Kolmogorov-Smirnov test

`ks.test()`

## Fisher’s F-Test

`var.test()`

## Chi-squared test

`chisq.test()`

## Analysis of Variance (ANOVA)

`aov()`


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