Viva and Thesis Defense Preparation for Quantitative Findings

The Statistical Questions Committees Actually Ask

Viva and defence questions about your quantitative findings cluster around a predictable set of themes, not the full breadth of statistics as a field:

• Why this test, and not an alternative? Be ready to explain why you chose, say, ANCOVA over plain ANOVA, or Mann-Whitney over the independent t-test, in terms of your specific data and design.
• Why this sample size? Connect your achieved sample directly back to your a priori power analysis, or explain honestly if it fell short and what that means for your findings.
• How did you handle assumption violations, if any occurred? Naming the violation and the corrective action you took (a non-parametric alternative, a robust correction, a transformation) shows methodological awareness; pretending nothing was violated when your own Chapter 4 reports otherwise does the opposite.
• What does this result actually mean, beyond statistical significance? Committees frequently probe practical significance and effect size, not just whether p was below .05.

Preparing a One-Page Statistical-Methods Summary

Build a single page, for your own use, not necessarily to hand in, that lists each research question, the test used, the key result, and the one-sentence justification for that test choice. Having this synthesised in one place means you’re not searching through your full Chapter 4 mid-defence to recall why you did what you did. This is also the fastest way to notice, before your committee does, any place where your justification is thinner than it should be.

Handling Questions About Assumption Violations

If an assumption was violated and you addressed it (switched tests, applied a correction, noted it as a limitation), that’s a defensible, normal part of real research; state it plainly rather than hoping it doesn’t come up. If you’re asked about an assumption you didn’t check, the honest answer, acknowledging the gap and reasoning through what it would mean, lands far better than an improvised justification that doesn’t hold up to a follow-up question.

Handling Questions About Sample Size and Power

If your achieved sample matched your a priori target, this is a short, confident answer. If it fell short, have your achieved power (or minimum detectable effect size) ready, calculated from your actual sample rather than avoided. See the full Chapter 5 discussion guide for how this same content should already appear in your limitations section, and PhD dissertation SPSS help for the broader statistical rigor doctoral committees expect.

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