Introduction to statease

In statease: Simplified Statistical Analysis with Plain-English Interpretation

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(statease)

Overview

statease is an R package designed to simplify statistical analysis by combining a wide range of tests with automatic plain-English interpretation of results. Whether you are a student, researcher, or educator, statease gives you numbers and meaning in one command.

---

Installation

# Install from CRAN
install.packages("statease")

# Load the package
library(statease)

---

Dataset

Throughout this vignette we use a simulated dataset of 90 students examining the effect of teaching methods on exam performance.

set.seed(42)

tutorial_data <- data.frame(
  student_id = 1:90,
  method     = rep(c("Traditional", "Online", "Hybrid"), each = 30),
  gender     = rep(c("Male", "Female"), times = 45),
  exam_score = c(
    round(rnorm(30, mean = 65, sd = 10)),
    round(rnorm(30, mean = 72, sd = 10)),
    round(rnorm(30, mean = 78, sd = 10))
  ),
  pre_test = c(
    round(rnorm(30, mean = 55, sd = 10)),
    round(rnorm(30, mean = 58, sd = 10)),
    round(rnorm(30, mean = 57, sd = 10))
  ),
  age        = round(rnorm(90, mean = 22, sd = 3)),
  passed     = rbinom(90, 1, prob = 0.7)
)

head(tutorial_data)

---

1. Descriptive Statistics

The describe() function provides a full summary of a numeric variable including measures of central tendency, spread, and a normality check.

result <- describe(tutorial_data$exam_score,
                   var_name = "Exam Score")
print(result)

You can also extract individual values:

result$mean
result$sd
result$skew_label

---

2. T-Tests

Independent Samples T-Test

Compare exam scores between male and female students:

males   <- tutorial_data$exam_score[tutorial_data$gender == "Male"]
females <- tutorial_data$exam_score[tutorial_data$gender == "Female"]

result <- ttest_interpret(
  males, females,
  var_name = "Exam Score by Gender"
)
print(result)

One-Sample T-Test

Test whether the average exam score is significantly different from 70:

result <- ttest_interpret(
  tutorial_data$exam_score,
  mu = 70,
  var_name = "Exam Score"
)
print(result)

Paired T-Test

Test whether students improved from pre-test to final exam:

result <- ttest_interpret(
  tutorial_data$exam_score,
  tutorial_data$pre_test,
  paired   = TRUE,
  var_name = "Score Improvement"
)
print(result)

---

3. One-Way ANOVA

Test whether teaching method affects exam scores:

result <- anova_interpret(
  exam_score ~ method,
  data = tutorial_data
)
print(result)

---

4. Two-Way ANOVA

Test the effect of both teaching method and gender on exam scores:

result <- anova2_interpret(
  exam_score ~ method * gender,
  data = tutorial_data
)
print(result)

---

5. MANOVA

Test the combined effect of teaching method on both exam score and pre-test score simultaneously:

result <- manova_interpret(
  cbind(exam_score, pre_test) ~ method,
  data = tutorial_data
)
print(result)

---

6. Chi-Square Test

Test whether there is an association between teaching method and pass/fail outcome:

tutorial_data$passed_label <- ifelse(tutorial_data$passed == 1,
                                      "Pass", "Fail")

result <- chisq_interpret(
  tutorial_data$method,
  tutorial_data$passed_label
)
print(result)

---

7. Correlation Analysis

Test the relationship between pre-test scores and exam scores:

result <- cor_interpret(
  tutorial_data$pre_test,
  tutorial_data$exam_score,
  var1_name = "Pre-Test Score",
  var2_name = "Exam Score"
)
print(result)

Spearman Correlation

result <- cor_interpret(
  tutorial_data$pre_test,
  tutorial_data$exam_score,
  method    = "spearman",
  var1_name = "Pre-Test Score",
  var2_name = "Exam Score"
)
print(result)

---

8. Simple Linear Regression

Predict exam score from pre-test score:

result <- reg_interpret(
  exam_score ~ pre_test,
  data = tutorial_data
)
print(result)

---

9. Multiple Linear Regression

Predict exam score from both pre-test score and age:

result <- mlr_interpret(
  exam_score ~ pre_test + age,
  data = tutorial_data
)
print(result)

---

10. Logistic Regression

Predict pass/fail outcome from pre-test score and age:

result <- logistic_interpret(
  passed ~ pre_test + age,
  data = tutorial_data
)
print(result)

---

11. Non-Parametric Tests

Mann-Whitney U Test

Non-parametric alternative to the independent samples t-test:

result <- mannwhitney_interpret(
  males, females,
  var_name = "Exam Score by Gender"
)
print(result)

Wilcoxon Signed Rank Test

Non-parametric alternative to the paired t-test:

result <- wilcoxon_interpret(
  tutorial_data$exam_score,
  tutorial_data$pre_test,
  var_name = "Score Improvement"
)
print(result)

Kruskal-Wallis Test

Non-parametric alternative to one-way ANOVA:

result <- kruskal_interpret(
  exam_score ~ method,
  data = tutorial_data
)
print(result)

---

12. P-Value Interpretation

Interpret any p-value in plain English:

result <- interpret_p(
  0.03,
  context = "teaching method effect on exam scores"
)
print(result)

---

13. The Master Function

The analyze() function automatically detects the right test based on your input — no need to remember which function to use!

# Descriptive statistics
analyze(x = tutorial_data$exam_score, var_name = "Exam Score")

# Auto t-test
analyze(
  x        = males,
  y        = females,
  var_name = "Exam Score by Gender"
)

# Auto ANOVA
analyze(formula = exam_score ~ method, data = tutorial_data)

# Auto non-parametric
analyze(
  formula  = exam_score ~ method,
  data     = tutorial_data,
  nonparam = TRUE
)

# Auto regression
analyze(formula = exam_score ~ pre_test, data = tutorial_data)

# Auto MANOVA
analyze(
  formula = cbind(exam_score, pre_test) ~ method,
  data    = tutorial_data
)

---

Summary

| Test | Function | analyze() support | |---|---|---| | Descriptive stats | describe() | ✔ | | Independent t-test | ttest_interpret() | ✔ | | One-sample t-test | ttest_interpret() | ✔ | | Paired t-test | ttest_interpret() | ✔ | | One-way ANOVA | anova_interpret() | ✔ | | Two-way ANOVA | anova2_interpret() | ✔ | | MANOVA | manova_interpret() | ✔ | | Chi-square | chisq_interpret() | ✔ | | Correlation | cor_interpret() | ✔ | | Simple regression | reg_interpret() | ✔ | | Multiple regression | mlr_interpret() | ✔ | | Logistic regression | logistic_interpret() | ✔ | | Mann-Whitney U | mannwhitney_interpret() | ✔ | | Wilcoxon Signed Rank | wilcoxon_interpret() | ✔ | | Kruskal-Wallis | kruskal_interpret() | ✔ | | P-value interpretation | interpret_p() | - |

statease documentation built on June 7, 2026, 5:06 p.m.