Null Hypothesis Significance Testing Task
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A Null Hypothesis Significance Testing Task is a method of statistical inference by which an observation is tested against a null hypothesis.
References
2015
- http://en.wikipedia.org/wiki/Null_hypothesis#Goals_of_null_hypothesis_tests
- Statistical tests can be significance tests or hypothesis tests. There are many types of [[Statistical hypothesis testing#Common test statistics|significance tests]] for one, two or more samples, for means, variances and proportions, paired or unpaired data, for different distributions, for large and small samples... All have null hypotheses. There are also at least 4 goals of null hypotheses for significance tests:[1]
- Technical null hypotheses are used to verify statistical assumptions. Example: The residuals between the data and a statistical model cannot be distinguished from random noise. If true, there is no justification for complicating the model.
- Scientific null assumptions are used to directly advance a theory. Example: The angular momentum of the universe is zero. If not true, the theory of the early universe may need revision.
- Null hypotheses of homogeneity are used to verify that multiple experiments are producing consistent results. Example: The effect of a medication on the elderly is consistent with that of the general adult population. If true, this strengthens the general effectiveness conclusion and simplifies recommendations for use.
- Null hypotheses that assert the equality of effect of two or more alternative treatments, for example, a drug and a placebo, are used to reduce scientific claims based on statistical noise. This is the most popular null hypothesis; It is so popular that many statements about significant testing assume such null hypotheses.
- Statistical tests can be significance tests or hypothesis tests. There are many types of [[Statistical hypothesis testing#Common test statistics|significance tests]] for one, two or more samples, for means, variances and proportions, paired or unpaired data, for different distributions, for large and small samples... All have null hypotheses. There are also at least 4 goals of null hypotheses for significance tests:[1]
- ↑ "Statistical Significance Tests". Br. J. Clin. Pharmac. 14: 325–331. 1982.
- (Leek & Peng, 2015) ⇒ Jeffrey T. Leek, and Roger D. Peng. (2015). “Statistics: P values are just the tip of the iceberg.” In: Nature, 520(7549).
- QUOTE: There is no statistic more maligned than the P value. Hundreds of papers and blogposts have been written about what some statisticians deride as 'null hypothesis significance testing' (NHST; see, for example, http://go.nature.com/pfvgqe). NHST deems whether the results of a data analysis are important on the basis of whether a summary statistic (such as a P value) has crossed a threshold. Given the discourse, it is no surprise that some hailed as a victory the banning of NHST methods (and all of statistical inference) in the journal Basic and Applied Social Psychology in February.