The method
1. Describe the variables first
Identify the measured outcome, units and comparison conditions. Read the numbers before explaining them. In a synthetic dataset, treat “arbitrary units” as a label for the given scale, not permission to import missing biological detail.
2. State the calculation explicitly
For a percentage decrease from control, subtract the comparison value from control and divide by control. Check whether the absolute difference or the proportional difference stays constant across rows. Those patterns are not interchangeable.
3. Name what the data cannot settle
A pattern can be consistent with several mechanisms. Without the required design information, replicates or uncertainty estimates, do not claim a unique mechanism, statistical significance or a broad real-world effect.
WORKED EXAMPLE
A synthetic assay
| Concentration (arbitrary units) | Control rate | Rate with X |
|---|---|---|
| 1 | 10 | 5 |
| 2 | 20 | 10 |
| 4 | 40 | 20 |
At concentration 2, the control rate is 20 and the rate with X is 10. The relative decrease is (20 − 10) / 20 = 50%. The treated rate is half the control at each tested concentration. The absolute differences are 5, 10 and 20, so they are not constant. These three rows alone do not establish an inhibition mechanism. They also supply no replicates or statistical test.
PUT IT INTO PRACTICE
Your workshop
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Check the reasoning
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Build your worksheet
Try a changed situation
A new table has control values 10, 20, 40 and comparison values 5, 15, 35. Is the percentage decrease constant? Explain.
Take away: First describe and calculate; then ask which explanations the design can distinguish.
Answer both learning checks to enable your review record.
Marking reviewed records your own activity. It does not establish mastery or readiness.