Normality Tests
Normality Tests is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Natural Sciences & Mathematics 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?
Normality Tests automatically applies all necessary assumption checks (normality, homogeneity of variance, etc.) for the relevant data type in the background and presents the results with a clear table + chart. Automated interpretation to 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 Natural Sciences & Mathematics 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 Normality Tests.
Panel assignments (form fields in the program):
- Column:
ornek_id - Significance (α):
0.05
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 — Natural Sciences & Mathematics
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 Natural Sciences & Mathematics:
Fen_Matematik/02_normality_chemical.xlsx
🎬 Scenario
We examine whether concentration, reaction rate and molecule weight are normally distributed. Because the validity of subsequent t-tests, ANOVA and correlation depends on this assumption, we test each variable separately.
⚙️ Variable Selection
- Variables: concentration_mM
- Variables: reaction_rate
- Variables: molecule_weight
Data Preview (First 5 Rows)
| sample_id | concentration_mM | reaction_rate | molecule_weight |
|---|---|---|---|
| 1.0 | 22.5 | 4.17 | 208.0 |
| 2.0 | 26.15 | 12.26 | 134.0 |
| 3.0 | 24.38 | 10.04 | 311.0 |
| 4.0 | 32.92 | 7.31 | 97.0 |
| 5.0 | 22.26 | 2.1 | 173.0 |
n = 200 · Columns: sample_id, concentration_mM, reaction_rate, molecule_weight
📈 MerQur Output
💬 Interpretation
We tested whether three continuous variables — concentration, reaction rate and molecule weight — are normally distributed. The result splits instructively: concentration is normal (p = 0.54), but reaction rate and molecule weight deviate significantly from normality (p < .001). This is typical in chemical data — reaction rates and weight distributions are often right-skewed. Practical upshot: we can safely use parametric tests (t-test, ANOVA, Pearson) on concentration; for reaction rate and molecule weight, nonparametric methods (Mann-Whitney/ Kruskal-Wallis) or a transform are more appropriate. The normality check is a critical preliminary step that decides, per variable, which test family fits.
⚠ 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 Natural Sciences & Mathematics file.
▶ Normality Tests — 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 2:40, where this analysis begins. Narration is in Turkish.
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📚 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.