Choosing the Right Statistical Test

What Is A Statistical Test?

Researchers use statistical tests when they want to:

• Compare two or more groups
• Check relationships between variables
• Predict outcomes
• Analyse proportions or frequencies in categories

Why Choosing The Right Statistical Test Matters

Selecting the correct statistical test is crucial because it directly affects the validity and credibility of your research. The wrong test can lead to misleading conclusions, incorrect interpretations, and weak results. Moreover, it helps you:

• Produce trustworthy and scientifically sound findings
• Avoid false positives or false negatives
• Strengthen your analysis section in dissertations, theses, or research papers

How To Choose The Right Statistical Test

Picking the right statistical test becomes easy when you follow a structured approach. Whether you are writing a dissertation, analysing survey data, or working on a research project, these steps help you quickly narrow down the correct test.

Step 1: Identify Your Research Question

The first step is to understand what you want to find out. Are you comparing groups? Testing relationships? Predicting an outcome?

Your research question determines the direction of your statistical analysis.

Step 2: Determine Your Variables (Categorical vs Continuous)

Identify the type of data you are working with:

• Categorical variables (e.g., gender, education levels, yes/no responses)
• Continuous variables (e.g., height, test scores, income)

Step 3: Check the Number of Groups or Conditions

Different tests are designed for different numbers of groups. For example, t-tests compare two groups, while ANOVA compares three or more. Ask yourself:

• Am I comparing two groups or more than two?
• Is there one condition or multiple conditions over time?

Step 4: Assess Normality and Distribution

Check if your data is normally distributed.

• Normally distributed data → Parametric tests (e.g., t-test, ANOVA)
• Non-normal or small sample sizes → Non-parametric tests (e.g., Mann–Whitney, Kruskal–Wallis)

Step 5: Decide if Data Is Related or Independent

Determine whether your groups are:

• Independent (different people in each group)
• Related/paired (same participants measured twice or matched pairs)

For example:

• Independent samples → Independent t-test
• Related samples → Paired t-test

Step 6: Choose Between Parametric vs Non-Parametric Tests

Your choice depends on:

• Distribution (normal or non-normal)
• Measurement scale
• Sample size
• Variance equality

Step 7: Match Your Goal (Compare, Correlate, Predict) to the Test

Finally, pick a test based on what you want to achieve:

• Compare groups → t-tests, ANOVA, Mann–Whitney, Kruskal–Wallis
• Measure relationships → Pearson, Spearman, Chi-square
• Predict outcomes → Regression (linear, logistic)
  

Types Of Statistical Tests With Examples

These tests help you compare mean scores or distributions across groups to see if the differences are statistically significant.

t-Test

A t-test is a parametric test used when comparing mean values of continuous data. It is ideal when your data is normally distributed.

1. Independent Samples t-Test

Used to compare the means of two independent groups.

Example: A dissertation comparing exam scores of male and female students to check if gender affects academic performance.

2. Paired Samples t-Test

Used when comparing two related measurements from the same participants.

Example: A study measuring stress levels before and after a mindfulness training programme.

3. One-Sample t-Test

Used to compare the mean of one group to a known or expected value.

Example: A research paper testing whether the average height of a sample of athletes differs from the national average.

ANOVA (Analysis of Variance)

ANOVA is used when comparing three or more groups. It checks whether there are significant differences between group means.

1. One-Way ANOVA

Used to compare three or more independent groups based on one factor.

Example: Comparing customer satisfaction levels across three different stores of the same brand.

2. Two-Way ANOVA

Used to compare groups based on two different independent variables.

Example: Investigating how gender (male/female) and training type (A/B) together affect employee performance.

3. Repeated-Measures ANOVA

Used when the same participants are measured multiple times (similar to paired t-test but with more than two measurements).

Example: Testing blood pressure at three stages: before treatment, mid-treatment, and post-treatment.

Mann–Whitney U Test (Non-Parametric)

A non-parametric alternative to the independent samples t-test. Used when data is non-normal or measured on an ordinal scale.

Example: Comparing satisfaction scores (ranked 1–5) between online shoppers and in-store shoppers.

Wilcoxon Signed-Rank Test

A non-parametric alternative to the paired t-test. Used when related samples are non-normal or ordinal.

Example: A dissertation comparing pre-test and post-test scores for a small group of participants after an intervention programme.

Kruskal–Wallis Test

A non-parametric alternative to one-way ANOVA. Used for comparing three or more independent groups.

Example: Comparing job satisfaction rankings across employees from three different departments.

Friedman Test

A non-parametric alternative to repeated-measures ANOVA. Used when the same participants are measured under three or more conditions with non-normal or ordinal data.

Example: Testing user experience scores for three versions of a website interface (Version A, B, and C) using the same group of participants.

Tests For Relationships Between Variables

These tests help determine whether two variables are connected and how strong that connection is. 

Correlation Tests

Correlation tests measure the strength and direction of a relationship between two variables.

1. Pearson Correlation (Parametric)

Used when both variables are continuous and normally distributed.

Example: Checking whether hours studied are related to exam scores among university students.

2. Spearman Correlation (Non-Parametric)

Used when data is non-normal, ordinal, or skewed.

Example: Examining the relationship between job satisfaction rankings and employee performance ratings.

3. Kendall’s Tau (Non-Parametric)

Ideal for small samples or data with many tied ranks.

Example: Studying the relationship between customer preference rankings and product quality ratings in a small pilot study.

Chi-Square Test (Test of Association)

The Chi-square test checks whether two categorical variables are associated.

Example: A research paper analysing whether gender is associated with preferred learning style (visual, auditory, kinaesthetic).

Tests For Predictions

Prediction tests estimate how well one or more variables can predict an outcome. These are essential for quantitative dissertations and applied research.

Regression Analysis

Regression models help you understand how changes in one variable affect another.

1. Simple Linear Regression

Used when you want to predict an outcome using one predictor variable.

Example: Predicting sales revenue based on advertising spend.

2. Multiple Linear Regression

Used when predicting an outcome using two or more predictors.

Example: Predicting employee performance from training hours, experience level, and motivation scores.

3. Logistic Regression

Used when the outcome variable is categorical (e.g., yes/no, pass/fail).

Example: Predicting the likelihood of a student passing an exam based on attendance and study habits.

Below are the most popular platforms students, researchers, and data analysts use for performing t-tests, ANOVA, correlations, regression, and more.

1. SPSS (IBM SPSS Statistics)

SPSS is one of the most widely used tools for academic research and dissertations.

• Point-and-click interface
• Easy menus for t-tests, ANOVA, regression, correlations
• Generates clean output and charts automatically

2. R (RStudio)

R is a powerful, free, open-source programming language for advanced statistical analysis.

• Highly flexible and customisable
• Thousands of statistical packages
• Ideal for complex models, visualisations, and big datasets

3. Python (With Pandas, SciPy, Statsmodels)

Python is one of the most popular languages for data science and machine learning.

• Easy to learn
• Excellent libraries for statistics (NumPy, SciPy, Statsmodels)
• Great for regression, correlations, time-series, and machine learning algorithms

4. Excel

Excel is a simple and accessible tool for basic statistical testing.

• Built-in functions for t-tests, correlations, regression
• Easy to visualise data with charts
• No coding required

5. JASP / Jamovi

Both JASP and Jamovi are free, open-source alternatives to SPSS with a clean, modern interface.

• Point-and-click interface
• Performs t-tests, ANOVA, regression, and non-parametric tests
• Automatically generates APA-style output

Frequently Asked Questions