Chi-Square Test with Plain-English Interpretation

Usage

chisq_interpret(x, y, correct = TRUE, conf.level = 0.95)

Arguments

x: A factor or character vector (first categorical variable)
y: A factor or character vector (second categorical variable)
correct: Logical. Apply Yates continuity correction. Default TRUE.
conf.level: Confidence level. Default 0.95.

Value

An object of class statease_chisq 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 <- chisq_interpret(x, y)
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
print(result)
#> 
#> -- statease Chi-Square Test Report ------------------------------
#>   N            : 10
#> -----------------------------------------------------------------
#>   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
#> 
#> -----------------------------------------------------------------
#>   Chi-square   : 0.000
#>   df           : 1
#>   p-value      : 1.0000
#>   Cramer's V   : 0.000 (negligible effect)
#> -----------------------------------------------------------------
#>   Interpretation:
#>   The result is not statistically significant (p = 1.0000 > alpha 0.05).
#>   There is no significant association between the two variables.
#>   Effect size is negligible (V = 0.000).
#> 
#>   WARNING: Some expected frequencies are less than 5. Interpret with caution.
#> -----------------------------------------------------------------
#>