DMAR (version 1.0.0)

cv_t: Provides the Critical Value(s) for a t-distribution

Description

Provides the Critical Value(s) for a t-distribution

Usage

cv_t(
  alpha_level,
  df,
  alternative = "not_equal",
  alpha_lower,
  alpha_upper,
  ncp = 0,
  verbose = TRUE
)

Value

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

Arguments

alpha_level

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

df

The number of degrees of freedom (a positive number)

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.

ncp

The noncentral parameter (if zero, the default, it is the central t-distribution).

verbose

Provides extra information about areas under the curve.

Author

Ken Kelley [email protected]

Details

Though a noncentral parameter can be included, that would not be done for a standard null hypothesis significance test.

References

Maxwell, S. E., Delaney, H. D., & Kelley, K. (2027). Designing experiments and analyzing data: A model comparison perspective (4th ed.). Routledge.

See Also

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

Examples

# A basic call for finding critical values with equal area in the two tails.
cv_t(alpha_level = .05, df = 13)

# A basic call for a single-sided confidence interval (for "a greater than" alternative hypothesis)
cv_t(alpha_level = .05, df = 13, alternative = "greater")

# A single-sided confidence interval (for "a greater than" alternative hypothesis); simple output.
cv_t(alpha_lower = 0, alpha_upper = .05, df = 13, verbose = FALSE)

# For a nonsymmetric 95% confidence interval.
cv_t(alpha_lower = .01, alpha_upper = .04, df = 13)