4.4 Adjusting statistical parameters to suit constraints
In Activity 6, you calculated a sample size for a given set of statistical parameters. However, you may have noticed that the required sample size is quite large. In general, you should always aim to match your resources to the required sample size. That is, if the required sample size is large, endeavour to seek enough funding or capacity to achieve this sample size.
However, key statistical parameters can be altered from an ideal value to something less-than-ideal in order to achieve a more practical sample size that is sufficient (if not perfect) for addressing the research topic or surveillance objective. Reducing precision, confidence level or power, or increasing the minimum difference to detect, will lead to lower sample size requirements.
Note though that in practice, confidence level is rarely set below 90%, and power is rarely set below 80%. As a general guide, you should aim to have 95% confidence, 80% power and 5% precision (where relevant), as well as a minimum detectable difference that is truly clinically meaningful (which might be as small as 5% or as large as 50%, depending on the topic).
Think back to the data presented in Activity 5, where there was a twofold difference between some farms and a sixfold difference between others. Which of these clinical differences would require a bigger sample size to demonstrate?
The twofold difference, because it is smaller.
Activity 7: Refining the sample size
Go back to the example and calculation you made in Activity 6. What happens if you:
- reduce only the confidence level to 90%?
- reduce only the power to 80%?
- increase only the proportion in the second province to 70%?
- make all three of the changes above?
Answer
- Changing only the confidence level to 90% reduces the total sample size to 3494.
- Changing only the power to 80% reduces the total sample size to 3210.
- Increasing only the proportion in the second province to 70% reduces the total sample size to 268.
- Making all three of the changes above reduces the total sample size to 166.
Were you surprised by how much the sample size changed when increasing the proportion in the second province? Try other changes to the parameter values and see what happens. Reflect on the guidance in the previous section about which parameters might be appropriate to change.
4.3 Choosing a sample size calculator
