A simple, step-by-step guide to selecting the correct statistical test for your data analysis in SPSS, STATA, or Excel.
Introduction
One of the most challenging aspects of research is choosing the right statistical test. Select the wrong test, and your results may be invalid, misleading, or rejected by reviewers. Select the right test, and you’ll produce credible, publishable findings.
This comprehensive guide will walk you through the process of selecting appropriate statistical tests based on your research objectives, data type, and distribution characteristics.
What is a Statistical Test?
A statistical test is a formal procedure used to decide whether your data support a hypothesis by comparing groups, examining relationships, or analyzing distributions. Statistical tests help you determine if patterns in your data are genuine or simply due to random chance.
As Field (2018) and Gravetter & Wallnau (2020) explain, selecting the right test ensures your results are valid, reliable, and meaningful.
6 Steps to Choose the Right Statistical Test
Step 1: Identify Your Research Purpose
Before selecting a test, ask yourself: What am I trying to find out?
Your research objective typically falls into one of these categories:
| Research Purpose | What You’re Doing | Example Research Question |
|---|
| Comparing groups | Testing if groups differ significantly | “Is there a difference in test scores between male and female students?” |
| Testing relationships | Examining if variables are related | “Is there a relationship between study hours and exam performance?” |
| Predicting outcomes | Using variables to predict another | “Can we predict job performance based on training hours and experience?” |
| Testing associations | Examining categorical relationships | “Is there an association between gender and career choice?” |
Step 2: Determine Your Data Type
Statistical tests are designed for specific types of data. Understanding your data type is crucial for test selection.
The Four Levels of Measurement (NOIR)
| Level | Description | Examples | Mathematical Operations |
|---|
| Nominal | Categories with no order | Gender, religion, nationality, blood type | Mode, frequency counts |
| Ordinal | Categories with meaningful order | Education level, satisfaction ratings, rankings | Median, percentiles |
| Interval | Equal intervals, no true zero | Temperature (°C), IQ scores, dates | Mean, standard deviation |
| Ratio | Equal intervals with true zero | Age, income, weight, height, time | All mathematical operations |
Quick Decision Rule:
- Nominal/Ordinal → Usually requires non-parametric tests
- Interval/Ratio → Can use parametric tests (if assumptions are met)
Step 3: Check the Number of Groups or Variables
How many groups are you comparing, or how many variables are you analyzing?
| Scenario | Number of Groups/Variables | Typical Tests |
|---|
| One group vs. population | 1 group | One-sample t-test |
| Two independent groups | 2 groups (different participants) | Independent t-test, Mann-Whitney U |
| Two paired/matched groups | 2 groups (same participants) | Paired t-test, Wilcoxon signed-rank |
| Three or more groups | 3+ groups | ANOVA, Kruskal-Wallis |
| Two continuous variables | 2 variables | Correlation (Pearson/Spearman) |
| Multiple predictors | 2+ independent variables | Multiple regression |
Step 4: Check Data Distribution (Parametric vs. Non-Parametric)
One of the most critical decisions is whether to use parametric or non-parametric tests.
Parametric Tests
- Assume data follows a normal distribution (bell curve)
- Require interval or ratio data
- Generally more powerful when assumptions are met
- Examples: t-test, ANOVA, Pearson correlation, linear regression
Non-Parametric Tests
- Make no assumptions about distribution
- Can be used with ordinal data
- Used when parametric assumptions are violated
- Examples: Mann-Whitney U, Kruskal-Wallis, Spearman correlation
How to Test for Normality in SPSS
- Go to:
Analyze → Descriptive Statistics → Explore
- Move your variable to the Dependent List
- Click
Plots and select Normality plots with tests
- Click
Continue then OK
Interpreting Normality Tests:
- Shapiro-Wilk test (for samples < 50): p > 0.05 = normal distribution
- Kolmogorov-Smirnov test (for samples ≥ 50): p > 0.05 = normal distribution
| If p-value is… | Distribution is… | Use… |
|---|
| > 0.05 | Normal | Parametric tests |
| < 0.05 | Not normal | Non-parametric tests |
Step 5: Match the Test to Your Objective
Use this comprehensive decision table to select your test:
Comparing Two Groups (Independent)
| Data Distribution | Test Name | SPSS Path |
|---|
| Parametric (normal) | Independent samples t-test | Analyze → Compare Means → Independent Samples T Test |
| Non-parametric | Mann-Whitney U test | Analyze → Nonparametric Tests → Legacy Dialogs → 2 Independent Samples |
When to use:
- Comparing mean scores between two different groups
- Example: Comparing test scores between treatment and control groups
Comparing Two Groups (Paired/Related)
| Data Distribution | Test Name | SPSS Path |
|---|
| Parametric (normal) | Paired samples t-test | Analyze → Compare Means → Paired Samples T Test |
| Non-parametric | Wilcoxon signed-rank test | Analyze → Nonparametric Tests → Legacy Dialogs → 2 Related Samples |
When to use:
- Comparing scores from the same participants at two time points
- Example: Pre-test vs. post-test scores after an intervention
Comparing Three or More Groups
| Data Distribution | Test Name | SPSS Path |
|---|
| Parametric (normal) | One-way ANOVA | Analyze → Compare Means → One-Way ANOVA |
| Non-parametric | Kruskal-Wallis H test | Analyze → Nonparametric Tests → Legacy Dialogs → K Independent Samples |
When to use:
- Comparing mean scores across three or more groups
- Example: Comparing satisfaction scores across four departments
Post-hoc tests: If ANOVA is significant, use Tukey HSD or Bonferroni to identify which groups differ.
Testing Relationships Between Two Variables
| Data Type | Test Name | SPSS Path |
|---|
| Both continuous (normal) | Pearson correlation | Analyze → Correlate → Bivariate |
| Ordinal or non-normal | Spearman correlation | Analyze → Correlate → Bivariate (select Spearman) |
Interpreting Correlation Strength:
| Correlation Coefficient (r) | Strength |
|---|
| 0.00 – 0.19 | Very weak |
| 0.20 – 0.39 | Weak |
| 0.40 – 0.59 | Moderate |
| 0.60 – 0.79 | Strong |
| 0.80 – 1.00 | Very strong |
Testing Association Between Categorical Variables
| Test Name | When to Use | SPSS Path |
|---|
| Chi-square test | Expected frequencies ≥ 5 in all cells | Analyze → Descriptive Statistics → Crosstabs |
| Fisher’s exact test | Expected frequencies < 5 in any cell | Analyze → Descriptive Statistics → Crosstabs → Exact |
When to use:
- Testing if there’s an association between two categorical variables
- Example: Is there an association between gender and voting preference?
Prediction and Regression
| Outcome Variable | Test Name | SPSS Path |
|---|
| Continuous (normal) | Linear regression | Analyze → Regression → Linear |
| Binary (0/1) | Logistic regression | Analyze → Regression → Binary Logistic |
| Count data | Poisson regression | Analyze → Generalized Linear Models |
When to use linear regression:
- Predicting a continuous outcome from one or more predictors
- Example: Predicting sales based on advertising spend and price
When to use logistic regression:
- Predicting a binary outcome (yes/no, pass/fail)
- Example: Predicting whether a customer will purchase (1) or not (0)
Step 6: Verify Assumptions Before Running Tests
Each statistical test has assumptions that must be checked:
Assumptions for Parametric Tests
| Assumption | How to Check | What to Do If Violated |
|---|
| Normality | Shapiro-Wilk test, histograms, Q-Q plots | Use non-parametric alternative |
| Homogeneity of variance | Levene’s test | Use Welch’s t-test or Games-Howell post-hoc |
| Independence | Study design review | Ensure observations are independent |
| Adequate sample size | Rule of thumb: n ≥ 30 per group | Collect more data or use non-parametric test |
Checking Assumptions in SPSS
For Normality:
Analyze → Descriptive Statistics → Explore → Plots → Normality plots with tests
For Homogeneity of Variance (Levene’s Test):
- Included automatically in Independent Samples t-test output
- For ANOVA: Click Options → Homogeneity of variance test
Quick Reference: Statistical Test Decision Flowchart
START: What is your research objective?
│
├─► COMPARING GROUPS
│ │
│ ├─► How many groups?
│ │ │
│ │ ├─► 2 groups
│ │ │ │
│ │ │ ├─► Same participants? → Paired t-test / Wilcoxon
│ │ │ └─► Different participants? → Independent t-test / Mann-Whitney
│ │ │
│ │ └─► 3+ groups → ANOVA / Kruskal-Wallis
│ │
├─► TESTING RELATIONSHIPS
│ │
│ ├─► Both variables continuous? → Pearson / Spearman correlation
│ └─► Both variables categorical? → Chi-square / Fisher's exact
│
└─► PREDICTING OUTCOMES
│
├─► Outcome continuous? → Linear regression
└─► Outcome categorical? → Logistic regression
Complete Statistical Test Summary Table
| Research Objective | Parametric Test | Non-Parametric Alternative |
|---|
| Compare 2 independent groups | Independent t-test | Mann-Whitney U |
| Compare 2 paired groups | Paired t-test | Wilcoxon signed-rank |
| Compare 3+ independent groups | One-way ANOVA | Kruskal-Wallis |
| Compare 3+ paired groups | Repeated measures ANOVA | Friedman test |
| Relationship between 2 continuous variables | Pearson correlation | Spearman correlation |
| Association between 2 categorical variables | Chi-square test | Fisher’s exact test |
| Predict continuous outcome | Linear regression | — |
| Predict binary outcome | Logistic regression | — |
Common Mistakes to Avoid
1. Using Parametric Tests with Non-Normal Data
Always check for normality before running t-tests or ANOVA. Violating this assumption can lead to incorrect conclusions.
2. Ignoring Sample Size Requirements
Many tests require minimum sample sizes. Small samples may lack statistical power to detect real effects.
3. Multiple Testing Without Correction
Running many tests increases the chance of false positives. Use Bonferroni correction or control the false discovery rate.
4. Confusing Correlation with Causation
A significant correlation does not prove that one variable causes changes in another.
5. Not Reporting Effect Sizes
P-values alone don’t tell the whole story. Always report effect sizes (Cohen’s d, r², η²) to show practical significance.
Quick Tips for Statistical Analysis
✓ Always check assumptions before selecting a test
✓ Use visualizations (boxplots, histograms) to inspect your data
✓ Report effect size alongside p-values
✓ Clearly state your test choice in your methodology section
✓ Use appropriate software (SPSS, STATA, R) for your analysis
✓ Consult a statistician for complex analyses
Reporting Statistical Results in APA Format
Independent t-test
An independent samples t-test revealed a significant difference in test scores between the treatment group (M = 78.5, SD = 12.3) and control group (M = 65.2, SD = 14.1), t(58) = 3.89, p < .001, d = 0.98.
ANOVA
A one-way ANOVA showed a significant effect of teaching method on student performance, F(2, 87) = 6.45, p = .002, η² = .13.
Correlation
There was a significant positive correlation between study hours and exam scores, r(48) = .65, p < .001.
Chi-square
A chi-square test of independence revealed a significant association between gender and career preference, χ²(2) = 8.92, p = .012, V = .28.
Regression
Linear regression analysis indicated that advertising spend significantly predicted sales, β = .72, t(98) = 9.34, p < .001, R² = .52.
Frequently Asked Questions
Q: What if my data is not normally distributed?
A: Use non-parametric alternatives. For example, use Mann-Whitney U instead of independent t-test, or Spearman instead of Pearson correlation.
Q: Can I use parametric tests with Likert scale data?
A: This is debated. Strictly speaking, Likert scales are ordinal, suggesting non-parametric tests. However, many researchers treat 5+ point scales as interval data and use parametric tests, especially with large samples.
Q: What sample size do I need?
A: It depends on the test and expected effect size. General guidelines:
- t-tests: minimum 30 per group
- ANOVA: minimum 20 per group
- Correlation: minimum 30 total
- Regression: minimum 10-20 cases per predictor variable
Q: How do I choose between Pearson and Spearman correlation?
A: Use Pearson for continuous, normally distributed data with linear relationships. Use Spearman for ordinal data, non-normal distributions, or non-linear monotonic relationships.
Q: What if Levene’s test is significant?
A: If Levene’s test shows unequal variances (p < .05), use Welch’s t-test instead of the standard independent t-test, or use Games-Howell post-hoc test for ANOVA.
Conclusion
Choosing the right statistical test is essential for valid research conclusions. By following the six-step process outlined in this guide—identifying your purpose, determining data type, checking group numbers, assessing distribution, matching the test, and verifying assumptions—you can confidently select appropriate statistical methods for your research.
Remember: when in doubt, consult with a statistician or data analyst before running your analysis.
Need Help with Statistical Analysis?
At Tobit Research Consulting, we specialize in helping researchers choose and run the right statistical tests. Our services include:
- SPSS Data Analysis
- STATA Analysis
- Statistical Consultation
- Results Interpretation
- Methodology Review
Contact us today for professional assistance with your research analysis.
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Last Updated: December 2025
Keywords: how to choose statistical test, statistical test selection, parametric vs non-parametric tests, t-test vs Mann-Whitney, ANOVA vs Kruskal-Wallis, correlation analysis, chi-square test, regression analysis, SPSS statistical tests, choosing the right statistical test
