
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.
Data Interpretation, Statistical, Confidence Intervals, Probability, Statistical Distributions
Data Interpretation, Statistical, Confidence Intervals, Probability, Statistical Distributions
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 17 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
