OptSig (version 1.0)

Opt.sig.norm.test: Optimal significance level calculation for the mean of a normal distribution (known variance)

Description

Computes the optimal significance level for the mean of a normal distribution (known variance)

Usage

Opt.sig.norm.test(d = NULL, n = NULL, p = 0.5, k = 1, alternative = "two.sided")

Arguments

d

Effect size

n

Sample size

p

prior probability for H0, default is p = 0.5

k

relative loss from Type I and II errors, k = L2/L1, default is k = 1

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less"

Value

alpha.opt

Optimal level of significance

beta.opt

Type II error probability at the optimal level

Details

Refer to Kim and Choi (2017) for the details of k and p

References

Kim and Choi, 2017, Choosing the Level of Significance: A Decision-theoretic Approach: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2652773

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale,NJ: Lawrence Erlbaum.

Stephane Champely (2017). pwr: Basic Functions for Power Analysis. R package version 1.2-1. https://CRAN.R-project.org/package=pwr

See Also

Kim and Choi, 2017, Choosing the Level of Significance: A Decision-theoretic Approach

Examples

Run this code
# NOT RUN {
Opt.sig.norm.test(d=0.2,n=60,alternative="two.sided")
# }

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