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NON-PARAMETRICmeasures which are not based upon the assumptions of the normal curve
KOLMOGOROV-SMIRNOVtest for comparing one sample design, ordinal scales
SIGN TESTtest for repeated measures, binary nominal scale; a version of the binomial distribution
MANN-WHITNEYa rank sum test for two independent samples
FRIEDMANa ranks test for repeated measures
KRUSKAL-WALLISa rank sum test for more than two independent samples
SPEARMANa correlation coefficient based upon ranks
ROBUSTnon-parametric tests are more cautious; they tend to avoid Type I error, therefore they are more
POWERFULparametric tests tend to avoid Type II error; they are more likely to reject the null and declare significance; therefore they are more
BINOMIALan exact non-parametric test appropriate for sample vs. norms, when all Bernoulli requirements are met
CHI SQUAREan inferential statistic for nominal rows and columns data
RELIABILITYconsistency of measurement
VALIDITYwhen the operational definition measures what it purports to measure
YATESdeveloped a correction formula for the two-by-two chi square
MCNEMARdeveloped a formula for a repeated measures chi square
SENSITIVEa measure with few false negatives
SPECIFICa measure with few false positives
RELIABILITYconsistency between measurements: the same subject gets a similar score on the same variable when measured again
VALIDITYwhen a measurement succeeds in measuring what it intends to measure
FALSE POSITIVEa case that is scored as having a characteristic, when actually it does not
FALSE NEGATIVEa case that is scored as not having a characteristic, when actually it does

Professor of Psychology
Crafton Hills College
Long Beach, CA

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