Independent Samples t-Test
Independent Samples t-Test is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Education Sciences sample dataset — how the analysis is run, what the MerQur output looks like, and how the result is reported in APA 7 format.
🎯 What is it for?
The Independent Samples t-Test automatically performs all required assumption checks (normality, homogeneity of variance, etc.) for the relevant data type in the background and presents the results with a clear table + chart. Automatic interpretation in the APA 7 standard, effect sizes such as Cohen’s d/η²/R², and 95% confidence intervals are reported.
📌 When is it used?
- Statistical analysis of measurements in the Education Sciences domain
- To produce APA 7-compatible result tables for academic publications
- Hypothesis testing and decision-making processes
- Undergraduate / master’s / PhD theses after the appropriate method has been selected
📐 Assumptions
- Appropriate scale — Variables must be at the measurement level required by the analysis (nominal/ordinal/interval/ratio)
- Independent observations — Observations must come from individuals independent of one another
- Sufficient sample size — The minimum n requirement for the analysis must be met
- Outlier check — Outliers must be detected and evaluated
If assumptions are violated, MerQur automatically suggests a non-parametric or robust alternative.
🛠 How to do it in MerQur
Load the data. Select the sample file from File → Open. MerQur auto-detects column types.
Select the analysis. From the left side select Independent Samples t-Test.
Panel assignments (form fields in the program):
- Group Column:
cinsiyet - Value Column:
matematik_puan - Equal variance:
False
Optional settings. Effect size ✓ · 95% confidence interval ✓ · Assumption checks (automatic).
▶ Run — click the button. Results are produced automatically as a table + chart.
📄 Export to Word. APA 7-formatted report with italic statistical symbols.
📊 Sample Dataset — Education Sciences
ℹ Note: The scenario, MerQur output and interpretation below were produced by actually running the real example dataset in MerQur. Numeric results on your own data will differ; the goal is to show how the analysis is set up and interpreted end-to-end.
🎬 Example File
This analysis is demonstrated on the following example dataset for Education Sciences:
Egitim_Bilimleri/04_independent_t_sex_math.xlsx
🎬 Scenario
A common question in education research is whether boys and girls differ in mathematics achievement. We have math scores from 115 students along with their sex, coded as female and male. We want to test whether the average math score differs between the two groups. An independent samples t-test fits this design because the outcome is continuous and we are comparing the means of two unrelated, independent groups.
⚙️ Variable Selection
- Grouping variable: sex (female, male)
- Test variable: math_score
- >> ADVANCED PARAMETERS (optional in the form — what they do):
- Hypothesis direction (two-sided / right / left).
- Variance assumption: Student (equal var) / Welch (unequal var — safer) / Auto (Levene decides).
- Effect sizes: Hedges g, Glass’s delta, CLES = P(X>Y).
- Effect-size CI; per-group Shapiro; Levene & Bartlett homogeneity.
- Per-group descriptives; Bootstrap CI for the mean difference.
Data Preview (First 5 Rows)
| student_id | sex | math_score | age |
|---|---|---|---|
| 14 | female | 80.9 | 16 |
| 3 | female | 69.6 | 16 |
| 26 | female | 69.6 | 17 |
| 17 | female | 78.5 | 17 |
| 20 | female | 70.0 | 18 |
n = 115 · Columns: student_id, sex, math_score, age
📈 MerQur Output
💬 Interpretation
We examined whether math scores differ by sex with an independent t-test. The difference is significant (t(113) = 2.52, p = 0.013), but the effect size is small-moderate at d = 0.47 — statistically real, but limited in magnitude. This distinction matters: in large samples even small differences can be significant, so it is essential to report effect size alongside the p-value. The independent t-test is the basic method for comparing the means of two independent groups (sex, class, school type). The small effect here says the sex difference exists but should not be exaggerated educationally. >> ADVANCED PARAMETERS (optional in the form — what they do): – Hypothesis direction (two-sided / right / left). – Variance assumption: Student (equal var) / Welch (unequal var — safer) / Auto (Levene decides). – Effect sizes: Hedges g, Glass’s delta, CLES = P(X>Y). – Effect-size CI; per-group Shapiro; Levene & Bartlett homogeneity. – Per-group descriptives; Bootstrap CI for the mean difference.
⚠ Common Mistakes
- Misidentifying the data type (e.g., loading a categorical variable as numeric)
- Skipping assumption checks and going straight to the p-value
- Failing to report effect size — APA 7 requires both p and effect size
- Failing to apply a Type I error correction (Bonferroni/Tukey) in multiple comparisons
- Not switching to a non-parametric alternative when n is insufficient
📹 Video Walkthrough
Watch the video below for an end-to-end walkthrough of this analysis on a Education Sciences file.
▶ Independent Samples t-Test — video walkthrough
This section is part of the Veriye İlk Bakış ve Parametrik Testler video (14 analyses in one video). The link below jumps straight to 7:10, where this analysis begins. Narration is in Turkish.
🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities & Admin Sciences · 🏃 Sport Sciences · 🌾 Agriculture, Forestry & Aquatic
📚 If You Used This Analysis, Cite MerQur
If you performed this analysis using MerQur in a scientific study, please use the citation below as part of your academic citation obligations (APA 7):
Örücü, Ö. K. (2026). MerQur: Integrated Academic Data Analysis & Reporting Platform [Computer software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001
For BibTeX, RIS, EndNote and the English citation form: all citation formats →
- American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
- Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.