Justify your alpha level by avoiding the Lindley paradox or aiming for moderate or strong evidence when using anova.

Justify your alpha level by avoiding the Lindley paradox or aiming for moderate or strong evidence when using anova.

ftestEvidence(evidence, df1, df2, paired = FALSE, printplot = FALSE)

Arguments

evidence	Desired level of evidence: "Lindley" to avoid the Lindley Paradox, "moderate" to achieve moderate evidence and "strong" to achieve strong evidence. Users that are more familiar with Bayesian statistics can also directly enter their desired Bayes factor.
df1	Numerator degrees of freedom.
df2	Denominator degrees of freedom.
paired	If true a within subjects design is assumed.
printplot	If true prints a plot relating Bayes factors and p-values.

Value

alpha level required for a two-sample t-test.

References

Maier & Lakens (2021). Justify Your Alpha: A Primer on Two Practical Approaches

Examples

## Avoid the Lindley paradox for an anova with 1 numerator and 248 denominator degrees of freedom.
ftestEvidence("lindley", 1, 248)
#> $alpha
#> [1] 0.01938688
#> 
#> $evidence
#> [1] 1
#>