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Some journals encouraged authors to do more detailed analysis than just a statistical significance test. In social psychology, the journal Basic and Applied Social Psychology banned the use of significance testing altogether from papers it published, [53] requiring authors to use other measures to evaluate hypotheses and impact. [54] [55]
Experimental methods are very popular in psychology, going back more than 100 years. Experimental psychology is a sub-discipline of psychology . Statistical methods applied for designing and analyzing experimental psychological data include the t-test, ANOVA, ANCOVA, MANOVA, MANCOVA, binomial test, chi-square, etc.
Statistical significance test: A predecessor to the statistical hypothesis test (see the Origins section). An experimental result was said to be statistically significant if a sample was sufficiently inconsistent with the (null) hypothesis.
Classical test theory assumes that each person has a true score,T, that would be obtained if there were no errors in measurement. A person's true score is defined as the expected number-correct score over an infinite number of independent administrations of the test.
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.
In broad usage, the "practical clinical significance" answers the question, how effective is the intervention or treatment, or how much change does the treatment cause. In terms of testing clinical treatments, practical significance optimally yields quantified information about the importance of a finding, using metrics such as effect size, number needed to treat (NNT), and preventive fraction ...
Test construction strategies are the various ways that items in a psychological measure are created and decided upon. They are most often associated with personality tests but can also be applied to other psychological constructs such as mood or psychopathology. There are three commonly used general strategies: inductive, deductive, and ...
In null-hypothesis significance testing, the p-value [note 1] is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct. [2] [3] A very small p-value means that such an extreme observed outcome would be very unlikely under the null hypothesis.