Assessment of hypothesis Flashcards
tests may be used for judging the significance of median, mode, coefficient of correlation and several other measures
PARAMETRIC TESTS
usually assume certain properties of the parent population from which we draw samples
Parametric tests or standard tests of hypotheses
require measurement equivalent to at least an interval scale
Parametric tests or standard tests of hypotheses
use statistical methods for testing hypotheses
Non-parametric tests or distribution-free test of hypotheses
assume only nominal or ordinal data
Non-parametric tests or distribution-free test of hypotheses
based on the normal probability distribution
z-test
used for judging the significance of several statistical measures, particularly the mean
z-test
comparing the mean of a sample to some hypothesised mean for the population in case of large sample, or when population variance is known
z-test
used for judging the significance of difference between means of two independent samples in case of large samples, or when population variance is known
z-test
used for comparing the sample proportion to a theoretical value of population proportion when n happens to be large
z-test
judging the difference in proportions of two independent samples when n happens to be large
z-test
based on t-distribution
t-test
appropriate test for judging the significance of a sample mean of small sample(s) when population variance is not known
t-test
for judging the significance of difference between the means of two samples in case of small sample(s) when population variance is not known
t-test
used for judging the significance of the coefficients of simple and partial correlations
t-test
In case two samples are related
for judging the significance of the mean of difference between the two related samples
paired t-test (difference test)
based on chi-square distribution
χ2 -test
used for comparing a sample variance to a theoretical population variance
χ2 -test
used for comparing a sample variance to a theoretical population variance
χ2 -test
based on F-distribution
F-test
used to compare the variance of the two-independent samples
F-test
used in the context of analysis of variance (ANOVA) for judging the significance of more than two sample means at one and the same time
F-test
used for judging the significance of multiple correlation coefficients
F-test
calculated and compared with its probable value for accepting or rejecting the null hypothesis
F-test
do not suppose any particular distribution and the consequential assumptions
NONPARAMETRIC TESTS
quick and easy to use do not require laborious computations since in many
cases the observations are replaced by their rank order and in many others we simply use signs
NONPARAMETRIC TESTS
When the measurements are not as accurate as is necessary for standard tests of significance
NONPARAMETRIC TESTS
apply to ordinal or nominal scale data
NONPARAMETRIC TESTS
if the population mean is the same as the hypothetical mean or not
SIGN TESTS
non parametric analogue of t test
SIGN TESTS
based on the direction of the plus or minus signs of observations in a sample and not on their numerical magnitudes
SIGN TESTS
This test is an analogue of paired t test
Two samples sign test (paired sign test)
/ Wilcoxon signed rank sum test (paired samples)
use this test when have two samples having paired observations
Two samples sign test (paired sign test)
for one sample t-test
Wilcoxon signed rank sum test (single sample)
for paired t- test
Wilcoxon signed rank sum test (paired samples)
used for ordered categorical data where a numerical scale is inappropriate, but it is possible to rank the observations
Both Wilcoxon test
analogue of t-test for two independent samples drawn from continuous populations
Mann Whitney U test
used to judge the randomness of a sample on the basis of the order in which the observations are taken
One Sample Runs Test
This is particularly true when we have little or no control over the selection of the data
One Sample Runs Test
This test is analogous to the one way analysis of variance
Kruskal Wallis test
Used to test the null hypothesis that ‘k’ independent samples come from identical universes against the alternative hypothesis that the medians of these universes are not equal
Kruskal Wallis test
distribution-free test used in testing a hypothesis concerning no difference among two sets of data
FISHER-IRWIN TEST
employed to determine whether one can reasonably assume
FISHER-IRWIN TEST
applicable for those situations where the observed result for each item in the sample can be classified into one of the two mutually exclusive categories
FISHER-IRWIN TEST
used when the data happen to be nominal and relate to two related samples
MC NEMER TEST
useful with before-after measurement of the same subjects
MC NEMER TEST
When the data are not available to use in numerical form for doing correlation analysis but when the information is sufficient to rank the data as first, second, third, and so forth
Spearman’s Rank Correlation
measure of association that is based on the ranks of the observations and not on the numerical values of the data
Spearman’s Rank Correlation
measure of correlation that exists between two sets of ranks of observations and not on the numerical values of data
Spearman’s Rank Correlation (rank correlation coefficient)
Test of a hypothesis concerning some single value for the given data
one-sample sign test
Test of a hypothesis concerning no difference among two or more sets of data
two- sample sign test
Fisher-Irwin test
Rank sum test
Non parametric
Test of a hypothesis of a relationship between variables
Rank correlation
Kendall’s coefficient of concordance
other tests for dependence
Test of a hypothesis concerning variation in the given data
test analogous to ANOVA = Kruskal-Wallis test.
Tests of randomness of a sample based on the theory of runs
one sample runs test
Test of hypothesis to determine if categorical data shows dependency or if two classifications are independent
chi-square test
