what stats test to use

3 min read 30-12-2024

Choosing the right statistical test is crucial for drawing accurate conclusions from your data. This guide helps you navigate the options, focusing on understanding your data and research question. Knowing what type of data you have (categorical or continuous) and the nature of your hypothesis (comparing groups, looking for correlations, etc.) is key.

Understanding Your Data: Categorical vs. Continuous

Before selecting a test, identify your data type:

Categorical Data: Represents categories or groups. Examples include gender (male/female), eye color (blue, brown, green), or treatment group (control, treatment A, treatment B). Categorical data can be further subdivided into nominal (unordered categories) and ordinal (ordered categories).
Continuous Data: Represents numerical values that can take on any value within a range. Examples include height, weight, temperature, or test scores. Continuous data can be measured on an interval scale (equal intervals but no true zero) or a ratio scale (equal intervals and a true zero).

Types of Statistical Tests and When to Use Them

The following outlines common statistical tests, categorized by research question and data type.

1. Comparing Two Groups

a) Independent Samples t-test: Used to compare the means of two independent groups with continuous data. For example, comparing the average test scores of students who received tutoring versus those who didn't. This test assumes the data is normally distributed.

b) Paired Samples t-test: Used to compare the means of two related groups with continuous data. For instance, comparing pre- and post-treatment scores for the same individuals. This accounts for the correlation between the two measurements.

c) Mann-Whitney U test (Wilcoxon Rank-Sum Test): A non-parametric alternative to the independent samples t-test used when data is not normally distributed or the assumptions of the t-test are violated. It compares the ranks of data in two groups.

d) Wilcoxon Signed-Rank Test: A non-parametric alternative to the paired samples t-test for non-normally distributed data. It compares the ranks of paired differences.

e) Chi-Square Test: Used to compare the proportions of categorical data between two independent groups. For example, comparing the proportion of men and women who prefer a particular brand.

2. Comparing More Than Two Groups

a) One-Way ANOVA (Analysis of Variance): Used to compare the means of three or more independent groups with continuous data. For example, comparing the average growth rates of plants under different fertilizer treatments. Assumes normal distribution and equal variances.

b) Kruskal-Wallis Test: A non-parametric alternative to one-way ANOVA for non-normally distributed data. It compares the ranks of data in multiple groups.

c) Repeated Measures ANOVA: Used to compare the means of three or more related groups with continuous data (e.g., measuring the same individuals at multiple time points).

d) Friedman Test: A non-parametric alternative to repeated measures ANOVA for non-normally distributed data.

3. Correlation

a) Pearson Correlation: Measures the linear association between two continuous variables. For example, assessing the relationship between height and weight. Assumes data is normally distributed and linearly related.

b) Spearman Correlation: A non-parametric alternative to Pearson correlation used for non-normally distributed data or when the relationship is not linear. It measures the monotonic association between two variables.

4. Regression Analysis

Regression analysis predicts the value of a dependent variable based on one or more independent variables. There are many types of regression, including:

Linear Regression: Predicts a continuous dependent variable based on one or more continuous or categorical independent variables.
Logistic Regression: Predicts a categorical dependent variable (often binary, e.g., yes/no) based on one or more independent variables.

Choosing the Right Test: A Decision Tree

To help guide your decision, consider this simplified decision tree:

What type of data do you have?
- Continuous: Proceed to question 2.
- Categorical: Use Chi-Square Test (for two groups) or Fisher's Exact Test (for small sample sizes).
How many groups are you comparing?
- Two: Use t-test (parametric) or Mann-Whitney U/Wilcoxon Signed-Rank test (non-parametric).
- More than two: Use ANOVA (parametric) or Kruskal-Wallis/Friedman test (non-parametric).
Are your groups independent or related?
- Independent: Use independent samples t-test or one-way ANOVA (parametric) or Mann-Whitney U test or Kruskal-Wallis test (non-parametric).
- Related: Use paired samples t-test or repeated measures ANOVA (parametric) or Wilcoxon Signed-Rank test or Friedman test (non-parametric).
Are you assessing a correlation or making a prediction?
- Correlation: Use Pearson or Spearman correlation.
- Prediction: Use regression analysis.

Note: This is a simplified guide. The best statistical test depends on the specifics of your research question and data. Always consult with a statistician if you have complex research designs or are unsure which test to use. Incorrect statistical analysis can lead to flawed conclusions. Using statistical software packages like SPSS, R, or SAS is recommended for accurate analysis.