In this second article in his new series, Stephen MacDonald moves on to hypothesis testing. Here, he looks at statistical assumptions, study design and data quality, which are central to arriving at the correct decision about what test to run, if at all.
Clinical laboratories generate and compare things constantly. Has a change in sample transport reduced turnaround time? Do specimens stored at room temperature and 4°C give meaningfully different results after 24 hours? Are three request locations associated with different processing times? Do two methods/lots of reagent perform the same? Has a change in workflow reduced the time between sample receipt and authorisation? There are also research-based projects where often the first question is… what statistical test do I need to do?
These are practical laboratory questions that affect all aspects of laboratory work. Yet the statistical method is often selected late in the project, sometimes only after the data have been collected. A project may be designed informally, data may be extracted because they are convenient, a test may be selected from a software menu, and the final conclusion may rest almost entirely on whether a P-value crosses 0.05.
That is far from ideal. The statistical method must be considered at the design stage, not bolted on at the end. Poor test selection can lead to false reassurance, unnecessary process changes, or overconfident conclusions from limited data. Proper statistical thinking can make even a modest laboratory project more defensible by clarifying the question, the comparison being made, the assumptions involved, the size of the observed effect, and the uncertainty around it.
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