Statistics for dummies

This is how we did it for ages

# devtools::install_github("neuropsychology/psycho.R")
library(psycho)

df <- psycho::affective
aov_results <- aov(Adjusting ~ Sex * Salary, data=df)
              Df Sum Sq Mean Sq F value Pr(>F)    
Sex           1   35.9   35.94  18.162 2.25e-05 ***
Salary        2    9.4    4.70   2.376   0.0936 .  
Sex:Salary    2    3.0    1.51   0.761   0.4674    
Residuals   859 1699.9    1.98                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
386 observations deleted due to missingness

And this is what R-bloggers recommends

analyze(aov_results)
The effect of Sex is significant (F(1, 859) = 18.16, p < .001) and can be considered as small (Partial Omega-squared = 0.019).
The effect of Salary is not significant (F(2, 859) = 2.38, p = 0.09°) and can be considered as very small (Partial Omega-squared = 0.0032).
The interaction between Sex and Salary is not significant (F(2, 859) = 0.76, p > .1) and can be considered as very small (Partial Omega-squared = 0).

Seriously!