IB Biology IA Troubleshooting

Fix

Run the statistical test anyway. Report the effect size and confidence interval. In Conclusion, propose two biologically plausible reasons why your data deviated from the prediction, and reference literature that also reports variance in this direction. Do NOT discard data.

Fix

If you still have time: add 2 more levels by interpolating (e.g. if you had 10, 20, 30 °C, add 5 °C and 25 °C). If you do not have time: shift the research question from trend-finding to comparison (t-test between 2 levels), and state this reframe explicitly in Research Design.

Fix

Quick fix: if you can still access the lab, add 2 more replicates at the extreme and middle levels — examiners weight the range endpoints heavily. No-lab fix: report the data you have with explicit confidence intervals, use non-parametric statistics (Mann-Whitney / Kruskal-Wallis), and list "only 3 replicates per level" as your top-ranked weakness in Evaluation.

Fix

Move biological background detail to a clearly labelled appendix (counted as 0 words). Rewrite Evaluation as a ranked list of the top 3 weaknesses with impact + improvement per bullet. Replace every discursive paragraph in Conclusion with a bulleted quantitative claim.

Fix

Report the p-value, the effect size (Cohen's d, η², or r²), and the confidence interval. In Conclusion state "no statistically significant effect was detected at α = 0.05" — and discuss why (small N, large variance, narrow IV range). Do not claim "no effect" unless your CI is very tight around zero.

Fix

Re-run with the correct test if you can (R, Python, Excel data-analysis ToolPak, or SPSS). Report both the original and corrected results, flag the correction in Evaluation, and explicitly cite why the new test fits your data structure.

Fix

Rewrite as: (1) the top 3 weaknesses you can identify; (2) for each, classify random vs systematic; (3) quantify expected impact on your conclusion (e.g. "would shift mean by ≈8 %"); (4) propose a specific, realistic improvement with a concrete mechanism (e.g. "use thermostatically controlled waterbath to reduce drift from ±2 °C to ±0.2 °C").

Fix

Never delete silently. Apply a pre-defined outlier rule (e.g. Tukey 1.5 × IQR), report which points it flags, run analysis with AND without the outliers, and state the decision rule. In Evaluation, discuss what specific methodological event likely produced them (contamination, temperature drift).

Fix

Add one diagram-free paragraph grounded in IB syllabus content (e.g. "enzyme denaturation above the optimum reflects loss of tertiary structure as hydrogen bonds break, reducing the number of functional active sites"). Cite 1–2 literature values for expected magnitude.

Fix

Rewrite as "How does [IV with units and range] affect [measurable DV with units] in [organism/system] under [constant conditions]?" Then justify the variable ranges and the organism choice.

Fix

Differentiate at the variable or organism level (different enzyme, different plant species, different buffer system). Rewrite the Introduction fully in your own biological framing. Use your own raw data only. If similarity is already suspected, talk to your IB coordinator immediately.

Fix

Ensure you (1) explicitly state the database, access date, and filtering criteria; (2) process the data (do not just download a pre-made graph); (3) run at least one statistical test; (4) build the biological rationale as strongly as for a wet-lab IA.