Paired Samples t-Test
Paired Samples t-Test is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Sport 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 Sport 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:
kuvvet_oncesi_kg - Column 2:
kuvvet_sonrasi_kg
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 — Sport 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 Sport Sciences:
Spor_Bilimleri/05_paired_t_force.xlsx
🎬 Scenario
We compare force measured before and after a program in the same athletes. Since the measures are paired, the paired t-test is appropriate.
⚙️ Variable Selection
- 1st measure / group: force_before_kg
- 2nd measure / group: force_post_kg
- >> 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)
| athlete_id | force_before_kg | force_post_kg | training_week |
|---|---|---|---|
| 1.0 | 153.4 | 173.6 | 8.0 |
| 2.0 | 128.0 | 146.6 | 8.0 |
| 3.0 | 150.7 | 164.4 | 8.0 |
| 4.0 | 102.1 | 123.9 | 8.0 |
| 5.0 | 128.9 | 142.9 | 8.0 |
n = 50 · Columns: athlete_id, force_before_kg, force_post_kg, training_week
📈 MerQur Output
💬 Interpretation
We paired and compared force (kg) measured before and after a training program in the same athletes. The result is very strong: mean difference -15.6 kg, t(49) = -24.94, p < .001, d_z = -3.53, a huge effect. Force rose significantly and substantially after the program. The paired t-test compares two timed measures on the same unit (before/after program); by isolating individual change it is more powerful than the independent test and is the right way to measure a training effect in sports science. >> 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 Sport 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 8:54, where this analysis begins. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 🏥 Health Sciences · 📊 Social, Humanities & Admin 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.