Provides the Critical Value(s) for the Standard Normal Distribution (the z-distribution, With Mean 0 and Variance 1)

Provides the Critical Value(s) for the Standard Normal Distribution (the z-distribution, With Mean 0 and Variance 1)

Usage

cv_z(
  alpha_level,
  alternative = "not_equal",
  alpha_lower,
  alpha_upper,
  verbose = TRUE
)

Arguments

alpha_level

Type I error rate (i.e., the false positive rate).

alternative

The type of alternative hypothesis of interest.

alpha_lower

The error rate on the lower (negative) side of the distribution.

alpha_upper

The error rate on the upper (positive) side of the distribution.

verbose

Provides extra information about areas under the curve.

Value

Returns the critical value(s), based on the input specifications, in a output style.

See also

Other critical values: cv_bonferroni_f(), cv_bryant_paulson(), cv_chisq(), cv_dunnett(), cv_f(), cv_scheffe(), cv_smm(), cv_t(), cv_tukey_hsd()

Author

Ken Kelley kkelley@nd.edu

Examples

# A basic call for finding critical values with equal area in the two tails.
cv_z(alpha_level = .05)
#>  term     value area_less area_greater
#>  lower_cv -1.96 0.025     0.975       
#>  upper_cv 1.96  0.975     0.025       

# A basic call for a single-sided confidence interval (for "a greater than" alternative hypothesis)
cv_z(alpha_level = .05, alternative = "greater")
#>  term     value area_less area_greater
#>  lower_cv -Inf  0         1           
#>  upper_cv 1.64  0.95      0.05        

# A single-sided confidence interval (for "a greater than" alternative hypothesis); simple output.
cv_z(alpha_lower = 0, alpha_upper = .05, verbose = FALSE)
#>  term     value
#>  lower_cv -Inf 
#>  upper_cv 1.64 

# For a nonsymmetric 95% confidence interval.
cv_z(alpha_lower = .01, alpha_upper = .04)
#>  term     value area_less area_greater
#>  lower_cv -2.33 0.01      0.99        
#>  upper_cv 1.75  0.96      0.04