Comments (1)
Thanks for pointing out this issue. I've now changed the default normality test from Shapiro-Wilk test to Anderson-Darling (AD) test. I am reluctant to use Kolmogorov-Smirnov (KS) test because-
- results from KS test can be pretty misleading (https://asaip.psu.edu/Articles/beware-the-kolmogorov-smirnov-test);
- AS is more powerful than KS
(Razali, Nornadiah; Wah, Yap Bee (2011). Power comparisons of Shapiro–Wilk, Kolmogorov–Smirnov, Lilliefors and Anderson–Darling tests. Journal of Statistical Modeling and Analytics. 2 (1): 21–33. )
At any rate, the normality test won't fail now, either in case of ggbetweenstats
or gghistostats
(if you have sample size less than 7, which AD test can't handle, the normality test won't be carried out):
library(ggstatsplot)
dat <- data.frame(x = c(rep(1, 2500), rep(2, 2501)), y = rnorm(5001))
ggstatsplot::ggbetweenstats(data = dat, x = x, y = y)
#> Warning: aesthetic `x` was not a factor; converting it to factor
#>
#> Reference: Welch's t-test is used as a default. (Delacre, Lakens, & Leys, International Review of Social Psychology, 2017).
#>
#> Note: Anderson-Darling Normality Test for y : p-value = 0.713
#>
#> Note: Bartlett's test for homogeneity of variances for factor x : p-value = 0.512
#>
ggstatsplot::gghistostats(data = dat, x = x)
#> Note: Anderson-Darling Normality Test for x : p-value = < 0.001
#>
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Created on 2018-03-18 by the reprex package (v0.2.0).
from ggstatsplot.
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