4.2 Statistical determinants of sample size
There are three key statistical parameters that have to be specified when determining sample size. In general, if a study sample is too small, the precision, confidence level and power of a study will fall short. This means you can’t reliably generalise the findings from your sample to the target population. Therefore, you need to understand and decide on appropriate values for each of these parameters in order to calculate the minimum sample size required.
For example, say that researchers want to determine the prevalence of resistance to oxytetracycline in E. coli isolates sampled from poultry farm workers who are potentially exposed to resistant isolates through their occupational contact with animals. The researchers plan to use an appropriate probability sampling strategy to select a representative sample of farms and individual workers. They decide they want their study results to be within 5% of the true prevalence (+/– 5%).
What is their desired precision?
Their desired precision is 0.05.
The
For example, say that researchers want to compare use of third-generation cephalosporins (3GCs) in Canadian and Australian hospitals. They are planning on using a probability sampling strategy to select a representative sample of hospitals in both countries. If a difference in use between the two countries is detected, they want to be 95% confident that this is because a difference truly does exist. What this means is that if the study shows a difference between the two countries, there is still a 5% probability that the difference found is due to chance (or luck) alone. For example, if all the Australian hospitals with high levels of 3GC use and all the Canadian hospitals with low levels of 3GC use happened to be selected, the results could suggest a difference that doesn’t actually exist.
The
4.1 Non-statistical considerations for determining sample size