Anomaly Detection
Anomaly Detection is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Health 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?
Anomaly Detection automatically performs all assumption checks required for the data type (normality, homogeneity of variance, etc.) in the background and presents the results in a clear table + chart. Automatic APA 7-formatted interpretation, effect sizes (Cohen’s d, η², R²) and 95% confidence intervals are reported.
📌 When is it used?
- Statistical analysis of measurements in the Health 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 Anomaly Detection.
Panel assignments (form fields in the program):
- Ozellik Sutunlari:
['WBC_K_uL'] - Yontem:
iqr - IQR carpan:
1.5 - Z esik:
3.0 - MAD esik:
3.5 - Contamination:
0.1 - Komsu:
20
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 — Health 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 Health Sciences:
Tip/99_anomali_lab_results.xlsx
🎬 Scenario
We detect unusual observations in multivariate laboratory data. For composite outlier detection, anomaly detection is appropriate.
⚙️ Variable Selection
- Variables: WBC_K_uL
- Variables: Hb_g_dL
- Variables: creatinine
- Variables: AST_U_L
- Variables: CRP_mg_L
Data Preview (First 5 Rows)
| patient_id | WBC_K_uL | Hb_g_dL | creatinine | AST_U_L | CRP_mg_L |
|---|---|---|---|---|---|
| 1.0 | 5.9 | 15.3 | 0.79 | 9.0 | 4.22 |
| 2.0 | 7.7 | 13.0 | 1.12 | 29.0 | 5.1 |
| 3.0 | 7.0 | 12.5 | 0.93 | 26.0 | 7.89 |
| 4.0 | 7.1 | 14.7 | 0.59 | 26.0 | 4.74 |
| 5.0 | 5.5 | 12.3 | 0.96 | 29.0 | 5.22 |
n = 200 · Columns: patient_id, WBC_K_uL, Hb_g_dL, creatinine, AST_U_L, CRP_mg_L
📈 MerQur Output
💬 Interpretation
We automatically detected unusual observations in multivariate laboratory data: 17 patients were flagged as anomalies. Anomaly detection finds observations that deviate markedly from normal (erroneous record, critical/ exceptional clinical picture) by evaluating several variables together; it catches “composite” outliers that univariate thresholds miss. In medicine it is used for laboratory quality control, erroneous-measurement detection and the early detection of critical patient profiles.
⚠ 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 Health Sciences file.
▶ Anomaly Detection — video walkthrough
A complete end-to-end walkthrough of this analysis in MerQur, narrated on screen. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 📊 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.