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The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. [3] Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below.
Test validity is the extent to which a test (such as a chemical, physical, or scholastic test) accurately measures what it is supposed to measure.In the fields of psychological testing and educational testing, "validity refers to the degree to which evidence and theory support the interpretations of test scores entailed by proposed uses of tests". [1]
The history of scientific method considers changes in the methodology of scientific inquiry, not the history of science itself. The development of rules for scientific reasoning has not been straightforward; scientific method has been the subject of intense and recurring debate throughout the history of science, and eminent natural philosophers and scientists have argued for the primacy of ...
The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In complex studies ...
Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10] As such, it compares estimates of pre- and post-test probability.
With the parallel test model it is possible to develop two forms of a test that are equivalent in the sense that a person's true score on form A would be identical to their true score on form B. If both forms of the test were administered to a number of people, differences between scores on form A and form B may be due to errors in measurement ...
The typical steps involved in performing a frequentist hypothesis test in practice are: Define a hypothesis (claim which is testable using data). Select a relevant statistical test with associated test statistic T. Derive the distribution of the test statistic under the null hypothesis from the assumptions.
For comparing significance tests, a meaningful measure of efficiency can be defined based on the sample size required for the test to achieve a given task power. [14] Pitman efficiency [15] and Bahadur efficiency (or Hodges–Lehmann efficiency) [16] [17] [18] relate to the comparison of the performance of statistical hypothesis testing procedures.