Quantitative results: report the numbers clearly and honestly

The results chapter should show what you found — not what you hoped to find. Clarity and honesty beat impressive p-values.

Choose the test that fits the design

The right analysis follows from data type and question: are you comparing two groups, associations between variables, or change over time? A t-test, ANOVA, chi-square and regression answer different things. Choosing a test after seeing what gives 'significance' is a classic mistake — decide the analysis from the design.

Report fully, not selectively

Report not just the p-value but also effect size, confidence interval and sample size. A statistically significant difference can be practically trivial. Use tables and figures to show the pattern, and let the text point to what matters most — don't repeat every cell of the table.

Keep a sharp line between results and interpretation. In the results chapter you describe what the numbers show; interpreting what they mean belongs in the discussion.

Be honest about the unexpected

Non-significant findings are findings too. Hiding them or hunting for an analysis that 'works' undermines the whole thesis. Examiners reward intellectual honesty — reporting what you found, including what didn't support your hypothesis.

Common pitfalls

• Choosing a test after seeing what yields significance.
• Reporting a p-value with no effect size or context.
• Mixing results and interpretation in the same chapter.

Ready to move on when…

• The analysis fits the data type and question.
• You report effect size, not just a p-value.
• Results and interpretation are clearly separated.