Statistical Analysis in Your Dissertation: Choosing the Right Test

Choosing the wrong statistical test is one of the more consequential mistakes a dissertation can contain, since it can undermine conclusions that otherwise rest on solid data collection. Yet many students choose a test based on what a classmate used, or what a tutorial happened to demonstrate, rather than what their specific research question and data actually require. This guide walks through how to choose correctly.

Start With Your Research Question, Not the Test

The starting point should always be your research question, translated into a specific statistical question - are you comparing groups, looking for a relationship between variables, or predicting an outcome from one or more predictors? Choosing a test before clarifying this underlying question is a common source of mismatched analysis, where the test technically runs but doesn't actually answer what the dissertation set out to ask.

Comparing Two Groups

If your question involves comparing two groups on a continuous outcome - for instance, comparing test scores between a treatment and control group - an independent samples t-test is the standard choice when your data is roughly normally distributed and the two groups are independent. If your two groups are actually the same participants measured at two time points (before and after an intervention), a paired samples t-test is the correct choice instead, not an independent samples test. If your data significantly violates normality assumptions, the non-parametric Mann-Whitney U test is the appropriate alternative for independent groups.

Comparing More Than Two Groups

When comparing three or more groups on a continuous outcome, one-way ANOVA is the standard parametric choice, followed by post-hoc tests (such as Tukey's HSD) to identify which specific groups differ if the overall ANOVA is significant. If your data violates normality assumptions, the Kruskal-Wallis test is the non-parametric alternative. If you are comparing groups across two independent variables simultaneously (for instance, treatment type and gender), a two-way ANOVA allows you to examine both main effects and any interaction between them.

Examining Relationships Between Variables

If your question asks whether two continuous variables are related, Pearson's correlation is the standard choice for normally distributed data, with Spearman's correlation as the non-parametric alternative. If you want to predict one continuous variable from one or more others, linear regression is the appropriate tool, while logistic regression is used when your outcome variable is categorical (typically binary, such as pass/fail or yes/no) rather than continuous.

Working With Categorical Data

When both your variables are categorical - for instance, examining whether there's an association between gender and preference for two treatment options - a chi-square test of independence is the standard approach, provided your expected cell counts meet the test's assumptions (typically at least 5 in most cells).

Checking Your Assumptions Before Choosing

Every parametric test carries assumptions - normality, homogeneity of variance, independence of observations - that should be checked before committing to a test, not assumed to hold automatically. Running a normality test (such as Shapiro-Wilk) and a variance-equality test (such as Levene's test) as a standard first step protects you from choosing a test whose assumptions your actual data doesn't meet, which can genuinely undermine your results if discovered later rather than checked upfront.

When to Use Non-Parametric Alternatives

When your data meaningfully violates the assumptions of a parametric test - notably non-normal distributions or small sample sizes - non-parametric alternatives exist for nearly every common parametric test, as referenced throughout this guide. These tests are generally less statistically powerful but make fewer assumptions about your data's underlying distribution, making them the more honest choice when assumptions are genuinely violated rather than forcing a parametric test regardless.

Reporting Your Chosen Test Correctly

Whatever test you choose, report it with the specific statistic, degrees of freedom, exact or appropriately rounded p-value, and an effect size measure (such as Cohen's d or eta-squared) alongside statistical significance - reporting p-values alone without effect sizes is increasingly considered incomplete reporting in most fields, since statistical significance alone doesn't convey the practical magnitude of an effect.

Common Mistakes in Dissertation Statistical Analysis

The most frequent mistake is choosing a test based on familiarity rather than fit to the actual research question and data type. A second is skipping assumption checks entirely and defaulting to a parametric test regardless of whether the data supports it. A third is reporting statistical significance without effect sizes, leaving the practical importance of a finding unclear. A fourth is running multiple tests without correcting for multiple comparisons, inflating the risk of false positive findings.

How CampusScribe Supports Statistical Analysis

CampusScribe's subject-specialist editors and consultants help dissertation students select appropriate statistical tests for their specific research questions and data, and review analysis write-ups for correct interpretation and reporting.

If you would like guidance choosing or reviewing a statistical approach for your dissertation, get in touch with our editing team.

Final Thoughts

The right statistical test follows directly from your research question, your data type, and your study design - not from familiarity or convenience. Check your assumptions before committing to a test, and report both significance and effect size so your findings communicate their real, practical importance, not just statistical existence.

Getting a Second Opinion on Your Analysis Plan

Before running your final analysis, it is worth having your planned approach reviewed by someone with statistical expertise, even briefly - a supervisor, a statistics consultant, or a knowledgeable peer. Catching a mismatched test or a violated assumption before you've built your entire results chapter around it saves considerably more time than discovering the issue during your defense. Ultimately, statistical rigor in a dissertation is judged less by the sophistication of the test chosen and more by how well that choice actually fits the question being asked - a simple, correctly applied test consistently outperforms an impressive-sounding one used incorrectly. That fit is worth checking twice before you commit real time to running the analysis. Getting this decision right early protects every conclusion that follows from it.

From choosing a topic to polishing a final draft, CampusScribe's subject-specialist editors and coaches help with dissertations, capstone projects, presentations, and more - whether you need topic guidance, structural feedback, or a final edit before your deadline.