Justify your alpha level by avoiding the Lindley paradox or aiming for moderate or strong evidence when using a t-test.

Justify your alpha level by avoiding the Lindley paradox or aiming for moderate or strong evidence when using a t-test.

ttestEvidence(
  evidence,
  n1,
  n2 = 0,
  one.sided = F,
  rscale = sqrt(2)/2,
  printplot = F
)

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.
n1

Sample size in Group 1.
n2

Sample size in Group 2. Leave blank for a one-sample or paired-sample
one.sided

Indicates whether the test is one sided or two sided.
rscale

Scale of the Cauchy prior
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 a two sample t-test with 300 participants per condition
ttestEvidence("lindley", 300, 300)

#> $alpha
#> [1] 0.02681291
#> 
#> $evidence
#> [1] 1
#>