Analyze research data

I have a spreadsheet of acoustic absorption tests with frequency bands, three trial runs per sample, and a control sample column. Send the cleaned, averaged results for each sample to Maya in the lab with a note that the control meets baseline. Sign off after you verify no blank cells in the trial columns and that the averaged values match the published formula, deadline Tuesday noon.

Before I send these research results to the PI and the journal reviewer, make the dataset easy to inspect: put the mean absorption by sample at the top, flag frequencies where trial variance exceeds 5%, highlight missing or out-of-range readings, and add a one-line summary of the cleanup steps so a reviewer can trust reproducibility.

The lab handed me raw absorption readings for 48 samples across octave bands, three trials each. I averaged the trials but eight samples show variance well above prior projects and two rows have a single zero likely from a sensor fault. I cannot tell whether the variance is biological or measurement error, and I must decide what to send to the PI tonight. Do I exclude the two zero rows and annotate the high-variance samples with a caveat, re-run the variance analysis with a robust estimator, or request repeat measurements? What is the most defensible next step given limited lab time?

Across projects I spend hours each week fixing the same issues: inconsistent trial headers, swapped control columns, and mixed units that break aggregated averages. I want one habit change that slashes this prep time and prevents last-minute queries from the PI. Which three checks or a single naming convention should I enforce at data collection so future sheets arrive analysis-ready?