Descriptive Statistics
Descriptive Statistics 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?
Descriptive Statistics 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 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 Descriptive Statistics.
Panel assignments (form fields in the program):
- This analysis takes no parameters (auto).
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/01_descriptive_site_inventory.xlsx
🎬 Scenario
We compiled the inventory of 280 sites/plots. Area, yield, quality, age and cost were measured for each. Before any inferential test we want the overall picture of the plot stock; so we begin with descriptive statistics.
⚙️ Variable Selection
- Variables: area_m2
- Variables: yield_unit
- Variables: quality_score
- Variables: age_year
- Variables: cost_TL
- Grouping (categorical): category
Data Preview (First 5 Rows)
| site_id | category | area_m2 | yield_unit | quality_score | age_year | cost_TL |
|---|---|---|---|---|---|---|
| 1 | field | 1067 | 53.8 | 2 | 59 | 5425 |
| 2 | forest | 1620 | 112.9 | 4 | 37 | 720 |
| 3 | field | 5087 | 98.4 | 5 | 26 | 12641 |
| 4 | field | 6447 | 38.6 | 2 | 10 | 14851 |
| 5 | forest | 4459 | 28.8 | 3 | 1 | 6857 |
n = 280 · Columns: site_id, category, area_m2, yield_unit, quality_score, age_year, cost_TL
📈 MerQur Output
💬 Interpretation
First we draw the overall picture of the site inventory: 280 plots, average area ~3,200 m2, yield unit ~50, quality score 3.38 of 5, average age 30 years and ~9,300 TL cost per plot. This descriptive table lays the groundwork for every analysis that follows — type/region comparisons, yield-input relationships, spatial site pattern. In field/agricultural engineering, before any inferential test, summarizing the plot stock’s basic features (area, yield, quality, cost) is essential both to audit data quality and to set planning priorities.
⚠ 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.
▶ Descriptive Statistics — 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 0:00, 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.