Independent Samples t-Test
Independent Samples t-Test is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Engineering 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 Engineering 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:
gübre - Value Column:
verim - 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 — Engineering
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 Engineering:
Muhendislik/04_independent_t_fertilizer_yield.xlsx
🎬 Scenario
We compare the mean yield of two fertilizer groups. With two separate groups and a continuous measure, the independent-samples t-test is appropriate.
⚙️ Variable Selection
- Grouping (categorical): fertilizer
- Test variable: yield
- >> 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)
| plot_id | fertilizer | yield | area_m2 |
|---|---|---|---|
| 14 | conventional | 398.1 | 1311 |
| 3 | conventional | 494.1 | 4886 |
| 26 | conventional | 351.7 | 2051 |
| 17 | conventional | 371.0 | 3571 |
| 20 | conventional | 423.8 | 1289 |
n = 115 · Columns: plot_id, fertilizer, yield, area_m2
📈 MerQur Output
💬 Interpretation
We compared the mean yield of two fertilizer groups (Welch correction was used because variances were unequal). The difference is significant and large: t(113) = -6.44, p < .001, d = -1.19. It shows the between-group difference is too pronounced to be chance and is also practically noteworthy. The independent-samples t-test is the standard way to compare the means of two separate groups (two fertilizers, two methods, two plot types) on a continuous measure; it is a fundamental tool for detecting intervention/treatment differences in the field. >> 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 Engineering 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 6:49, where this analysis begins. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · 🏥 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.