Fisher's Exact Test with Plain-English Interpretation

Usage

fisher_interpret(
  x,
  y,
  conf.level = 0.95,
  simulate.p.value = FALSE,
  context = NULL
)

Arguments

x: A factor or character vector (first categorical variable)
y: A factor or character vector (second categorical variable)
conf.level: Confidence level. Default 0.95.
simulate.p.value: Logical. Whether to use simulation to compute p-values for larger tables. Default FALSE.
context: Optional description of the study design or sampling method, echoed back in the printed report. Default NULL.

Value

An object of class statease_fisher containing test results and interpretation. Use print() to display the formatted report.

Examples

x <- c("Yes","No","Yes","Yes","No","Yes","No","No","Yes","Yes")
y <- c("Male","Female","Male","Female","Male",
       "Female","Male","Female","Male","Female")
result <- fisher_interpret(x, y)
print(result)
#> 
#> --- statease Fisher's Exact Test Report ---------------
#>   N            : 10
#>   Table size   : 2 x 2
#> 
#>   Contingency Table (Observed):
#>      y
#> x     Female Male
#>   No       2    2
#>   Yes      3    3
#> 
#>   Expected Frequencies:
#>      y
#> x     Female Male
#>   No       2    2
#>   Yes      3    3
#> 
#> -----------------------------------------------------------------
#>   p-value      : 1.0000
#>   Odds Ratio   : 1.000
#>   95% CI      : [0.042, 23.671]
#> -----------------------------------------------------------------
#>   Assumption Checks:
#>     Expected cell frequencies : NOTE     (one or more cells < 5 (Fisher's test appropriate for this))
#>     Sample independence       : NOTE     (assumed from study design, not testable from data)
#> 
#>   NOTE: Assumption checks are diagnostic tools and may be
#>   influenced by sample size and other characteristics of the
#>   data. Passing a check does not prove that an assumption is
#>   satisfied, and a warning does not automatically invalidate
#>   the analysis. Interpret these results alongside your
#>   knowledge of the data.
#> -----------------------------------------------------------------
#>   Interpretation:
#>   The result is not statistically significant (p = 1.0000 > alpha 0.05).
#>   There is insufficient evidence of an association between the two variables.
#>   OR = 1.000: No association between exposure and outcome.
#>   95% CI [0.042, 23.671] includes 1: No statistically significant evidence of an association.
#> -----------------------------------------------------------------
#>