With respect to a clinical trial, define Type 1 and Type 2 Error, the Point Estimate and Confidence Interval (40% marks). Discuss the relevance of Power to study design and interpretation of the results (60% marks)
Most candidates correctly distinguished between a type 1 and type 2 errors. Type I error is:
known as α and is when you incorrectly reject the null hypothesis – that is say a difference exists when it actually doesn’t. Type II error is: when you incorrectly accept the null hypothesis, is termed β, that is to say no difference exists when it actually does. The point estimate: Is a single value estimate of a population parameter. It represents a descriptive statistic for a summary measure, or a measure of central tendency from a given population sample. Confidence intervals: define a range of values that are likely to include a population parameter. They are derived from the standard error for a given population. The percentage given, eg. 95 % reflects the probability that the true value will be contained within that interval.
Few candidates gave a definition for power or a value. Power is the likelihood of detecting a
specified difference if it exists. It is important as it is a key determinant of the Sample size
required for a study and this is a vital aspect of experimental design and evaluation. A sample size can to be too small – so can’t adequately rule in / out an effect (ie. the study will lack the precision to provide reliable answers). If too large, a study will enrol unnecessary subjects to an experiment and this wastes time uses excess resources. It is unethical to conduct studies in both these cases.
This college answer possesses many of the characteristic of an "ideal" college comment, i.e. it is informative with regards to what was expected, and resembles a "model answer" in its content.
To answer the question in some detail:
Type 1 error
Type 2 error
The influence of power on study design
The influence of power on study interpretation
Higgins, Julian PT, and Sally Green, eds. Cochrane handbook for systematic reviews of interventions. Vol. 5. Chichester: Wiley-Blackwell, 2008.
Morris, Julie A., and Martin J. Gardner. "Statistics in Medicine: Calculating confidence intervals for relative risks (odds ratios) and standardised ratios and rates." British medical journal (Clinical research ed.) 296.6632 (1988): 1313.
Campbell, Michael J., and Martin J. Gardner. "Statistics in Medicine: Calculating confidence intervals for some non-parametric analyses." British medical journal (Clinical research ed.) 296.6634 (1988): 1454.
Cohen, Jacob. "Statistical power analysis." Current directions in psychological science 1.3 (1992): 98-101.