Biology Data Collection and Statistical Analysis

Proof required

Submit a written protocol or dataset justification (2–3 pages) covering: your biological question, your variables and sampling strategy (field/lab) or dataset source and accession, your rationale for the collection design or dataset selection, and your planned statistical analysis approach.

What gets checked

• Biological question is specific and testable — 'Does species diversity correlate with habitat edge distance in urban parks?' not 'What lives in parks?'
• Protocol or dataset selection includes a clear justification — why this design or dataset can answer the question, not just that it exists
• Statistical approach is named at proposal stage — t-test, ANOVA, regression, species diversity index — confirms the submitter has thought through the analysis before collecting

Common mistakes

• Designing a protocol without considering what statistical test the data will support — data collected without a planned analysis often cannot answer the intended question
• Selecting a public dataset because it was the first result without checking whether it contains the variables needed to address the question

Resources

What a verifier looks for

• Ask the submitter to state their null hypothesis in one sentence — confirms the question is genuinely testable.
• Ask why they chose this sampling strategy or dataset over alternatives — a considered choice indicates genuine understanding of the options.
• Ask what statistical test they plan to use and why it is appropriate for their data type — a correct justification (e.g., 'I'll use Mann-Whitney because my count data is not normally distributed') indicates methodological literacy.

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Proof required

Submit your raw data table or extracted dataset file, your R or Python code for the initial analysis (or a spreadsheet with clearly labelled calculations), and at least one results figure with a caption explaining what it shows.

What gets checked

• Raw data is submitted alongside the analysis code — the analysis should be reproducible from the data provided
• At least one figure is provided with an accurate caption that describes what the figure shows, not what the submitter hopes it shows
• Analysis code or spreadsheet is readable and structured — not a single unlabelled script with no comments indicating what each step does

Common mistakes

• Collecting data without a structured recording method and then trying to analyse an inconsistently formatted spreadsheet — data entry format decisions must be made before collection, not after
• Reporting preliminary results as conclusions before running the planned statistical test — initial figures are exploratory, not confirmatory

Resources

What a verifier looks for

• Ask the submitter to explain what the figure shows and what they would conclude from it provisionally — a genuine analyst gives a hedged interpretation, not a confident conclusion from preliminary data.
• Ask what the biggest data quality issue they encountered was and how they handled it — realistic data collection always surfaces quality problems.
• Ask whether the preliminary results are in the direction they expected — and if so, ask how they would guard against confirmation bias in the final analysis.

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Proof required

Submit your final analysis report (4–6 pages: background question, methods, results, discussion, limitations) and a written record of your Q&A session: the specific questions the reviewer raised about your methodology or interpretation and your responses. The reviewer must be named and their biological background stated.

What gets checked

• Report includes a limitations section that honestly acknowledges what the analysis cannot conclude — not just what it found
• Q&A record shows at least three specific methodological challenges from the reviewer and substantive responses from the submitter
• Reviewer is a biologist, ecologist, or field scientist — not a general academic or a friend without domain knowledge

Common mistakes

• Writing a discussion section that only confirms the hypothesis without engaging with alternative explanations — a genuine scientific discussion considers what might have produced the same data by a different mechanism
• Using a reviewer who agrees with everything without challenging any methodological choice — the Q&A must surface at least one real challenge

Resources

What a verifier looks for

• Ask the submitter what the reviewer's strongest challenge was and how they responded — someone who went through a genuine Q&A can recall this clearly.
• Ask whether the reviewer identified any limitation the submitter had not included in their report — an honest 'yes' indicates a genuine exchange.
• Verify the reviewer is a biologist with specific field or laboratory experience relevant to the data type — general academics without biological research experience are not sufficient for a methodology Q&A on ecological or molecular data.

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