# optimal_sample: Justify your alpha level by minimizing or balancing Type 1... In JustifyAlpha: Justifying Alpha Levels for Hypothesis Tests

## Description

Justify your alpha level by minimizing or balancing Type 1 and Type 2 error rates.

## Usage

 ```1 2 3 4 5 6 7 8``` ```optimal_sample( power_function, errorgoal = 0.05, costT1T2 = 1, priorH1H0 = 1, error = "minimize", printplot = FALSE ) ```

## Arguments

 `power_function` Function that outputs the power, calculated with an analytic function. `errorgoal` Desired weighted combined error rate `costT1T2` Relative cost of Type 1 errors vs. Type 2 errors. `priorH1H0` How much more likely a-priori is H1 than H0? `error` Either "minimize" to minimize error rates, or "balance" to balance error rates. `printplot` Print a plot to illustrate the alpha level calculation. This will make the function considerably slower.

## Value

Returns a list of the following alpha = alpha or Type 1 error that minimizes or balances combined error rates, beta = beta or Type 2 error that minimizes or balances combined error rates, errorrate = weighted combined error rate, objective = value that is the result of the minimization, either 0 (for balance) or the combined weighted error rates, samplesize = the desired samplesize. plot = plot of alpha, beta, and error rate as a function of samplesize (only if printplot = TRUE)

## References

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

## Examples

 ```1 2 3 4 5 6 7 8``` ```## Optimize power for a independent t-test, smallest effect of interest ## d = 0.5, desired weighted combined error rate = 5% res <- optimal_sample(power_function = "pwr::pwr.t.test(d = 0.5, n = sample_n, sig.level = x, type = 'two.sample', alternative = 'two.sided')\$power",errorgoal = 0.05) res\$alpha res\$beta res\$errorrate res\$samplesize ```

JustifyAlpha documentation built on Sept. 15, 2021, 9:08 a.m.