Paired Samples t-Test
Paired 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 Paired 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 Paired Samples t-Test.
Panel assignments (form fields in the program):
- Column 1:
on_test - Column 2:
son_test
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/05_paired_t_on_last_test.xlsx
🎬 Scenario
We ran an instructional intervention and want to know whether students improved. The same 50 students were measured twice: an early test and a final test, with the intervention happening in between. Because each student provides both an early and a final score, the two measurements are paired within the same person. A paired-samples t-test is appropriate here since we are comparing two related measurements taken from the same students.
⚙️ Variable Selection
- Pair measurement 1 (M1): on_test
- Pair measurement 2 (M2): last_test
- >> ADVANCED PARAMETERS (optional in the form — what they do):
- Hypothesis direction (two-sided / right / left).
- Effect sizes: Hedges g; d_av (standardized by the average SD).
- Effect-size CI; pairwise correlation between the two measures.
- Shapiro / K-S on the differences; descriptives; Bootstrap CI of the mean difference.
Data Preview (First 5 Rows)
| student_id | name_surname | on_test | last_test | intervention_week |
|---|---|---|---|---|
| 1 | student-1 | 47.1 | 63.6 | 8 |
| 2 | student-2 | 54.0 | 68.9 | 8 |
| 3 | student-3 | 66.5 | 86.4 | 8 |
| 4 | student-4 | 51.1 | 65.8 | 8 |
| 5 | student-5 | 57.3 | 65.1 | 8 |
n = 50 · Columns: student_id, name_surname, on_test, last_test, intervention_week
📈 MerQur Output
💬 Interpretation
We measured the effect of an intervention (extra instruction, new method) by comparing the same students’ pre- and post-test scores with a paired t-test. The result is striking: post-test scores are on average 11.72 points higher than pre-test, t(49) = -17.82, p < .001, with an enormous effect size (d_z = -2.52). The intervention raised achievement very strongly. Because we measured the same students twice, the paired test is correct — it removes between-student variability and focuses only on the individual’s own change. It is the gold-standard way to evaluate before/after interventions in education. >> ADVANCED PARAMETERS (optional in the form — what they do): – Hypothesis direction (two-sided / right / left). – Effect sizes: Hedges g; d_av (standardized by the average SD). – Effect-size CI; pairwise correlation between the two measures. – Shapiro / K-S on the differences; descriptives; Bootstrap CI of 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.
▶ Paired 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 9:10, 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.