In support of null hypothesis significance testing.
Many criticisms have been levelled at null hypothesis significance testing (NHST). It is argued here that although there is reason to doubt that data subjected only to NHST have been subjected to sufficient analysis, the search for clear answers to well-formulated questions derived from substantive hypotheses is well served by NHST. To reliably draw inferences from data, however, NHST may need to be complemented by additional methods of analysis, such as the use of confidence intervals and of estimates of the degree of association between independent and dependent variables. It is argued that these should be seen as complements of, rather than as substitutes for, NHST since they do not directly test the strength of evidence against a null hypothesis.
Mogie
Michael
M
Centre for Mathematical Biology, Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, UK. m.mogie@bath.ac.uk
D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D011336:Probability; D016008:Statistical Distributions