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Extensions to the theory of hypothesis testing include the study of the power of tests, i.e. the probability of correctly rejecting the null hypothesis given that it is false. Such considerations can be used for the purpose of sample size determination prior to the collection of data.
Testing a hypothesis suggested by the data can very easily result in false positives (type I errors). If one looks long enough and in enough different places, eventually data can be found to support any hypothesis. Yet, these positive data do not by themselves constitute evidence that the hypothesis is correct. The negative test data that were ...
John W. Tukey wrote the book Exploratory Data Analysis in 1977. [6] Tukey held that too much emphasis in statistics was placed on statistical hypothesis testing (confirmatory data analysis); more emphasis needed to be placed on using data to suggest hypotheses to test.
Classical hypothesis testing, for instance, has often relied on the assumption of data normality. To reduce reliance on this assumption, robust and nonparametric statistics have been developed. Bayesian statistics, on the other hand, interpret new observations based on prior knowledge, assuming continuity between the past and present.
Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. [1] Data is collected and analyzed to answer questions, test hypotheses, or disprove theories. [11] Statistician John Tukey, defined data analysis in 1961, as:
This calls for a need to conceptually switch from hypothesis-driven studies to hypothesis-generating research which is discovery-based. [4] Normally, discovery-based approaches for research are initially hypothesis-free, however, hypothesis testing can be elevated to a new level that effectively supports traditional hypothesis-driven studies. [11]
The hypothesis of Andreas Cellarius, showing the planetary motions in eccentric and epicyclical orbits. A hypothesis (pl.: hypotheses) is a proposed explanation for a phenomenon. A scientific hypothesis must be based on observations and make a testable and reproducible prediction about reality, in a process beginning with an educated guess or ...
Theory-driven evaluation (also theory-based evaluation) is an umbrella term for any approach to program evaluation that develops a theory of change and uses it to design, implement, analyze, and interpret findings from an evaluation. [1] [2] [3] More specifically, an evaluation is theory-driven if it: [4]