Standard cut-offs are not recommended when determining a suitable effect size for a power analysis. Indeed, the ‘meaningfulness’ of an effect size will depend on some subjective elements. That is, a ‘small’ effect may have drastic implications in certain contexts, while ‘large’ effects may have little to no implications in other contexts. A recent publication has provided a practical example to help differentiate a statistically versus a clinical meaningful effect size.

Shiny applications offer a fantastic way to produce interactive web applications. Unsurprisingly, there are abundant R packages built specifically for Shiny to facilitate ‘telling your data story’. For example, I often use the pickerInput() function from the shinyWidgets package, which is more aesthetic and has built-in ‘Select All/None’ buttons compared to the base checkboxGroupInput().
pickerInput(inputId = 'picker', label = 'Please choose your options', choices = c("Plot", "Text", "Analysis"), multiple = T, options = list(`actions-box` = TRUE)) Please choose your options Plot Text Analysis This basic interface allows users to select any combination of selections.

Chi-square Chi-square tests are common in psychological science (Bakker & Wicherts, 2011). These tests compare the observed (i.e., the actual frequency) versus the expected (i.e., \(expected_{i,j} = \frac{n_{rowi}*n_{colj}}{n_{tot}}\)) frequencies in a \(Row* Column\) contingency tables and are sometimes referred to as crosstabs (e.g., SPSS). Formally, the Chi-square statistic is defined as:
\(\chi^2 = \Sigma\frac{(O-E)^2}{O}\)
with degrees of freedom:
\(df = (n_{rows}-1)*(n_{cols}-1)\)
Despite the ubiquity of these tests, post-hoc analyses may be less common.