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Research > Assessment of hypothesis > Flashcards

Assessment of hypothesis Flashcards

1
Q

tests may be used for judging the significance of median, mode, coefficient of correlation and several other measures

A

PARAMETRIC TESTS

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2
Q

usually assume certain properties of the parent population from which we draw samples

A

Parametric tests or standard tests of hypotheses

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3
Q

require measurement equivalent to at least an interval scale

A

Parametric tests or standard tests of hypotheses

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4
Q

use statistical methods for testing hypotheses

A

Non-parametric tests or distribution-free test of hypotheses

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5
Q

assume only nominal or ordinal data

A

Non-parametric tests or distribution-free test of hypotheses

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6
Q

based on the normal probability distribution

A

z-test

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7
Q

used for judging the significance of several statistical measures, particularly the mean

A

z-test

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8
Q

comparing the mean of a sample to some hypothesised mean for the population in case of large sample, or when population variance is known

A

z-test

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9
Q

used for judging the significance of difference between means of two independent samples in case of large samples, or when population variance is known

A

z-test

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10
Q

used for comparing the sample proportion to a theoretical value of population proportion when n happens to be large

A

z-test

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11
Q

judging the difference in proportions of two independent samples when n happens to be large

A

z-test

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12
Q

based on t-distribution

A

t-test

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13
Q

appropriate test for judging the significance of a sample mean of small sample(s) when population variance is not known

A

t-test

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14
Q

for judging the significance of difference between the means of two samples in case of small sample(s) when population variance is not known

A

t-test

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15
Q

used for judging the significance of the coefficients of simple and partial correlations

A

t-test

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16
Q

In case two samples are related

for judging the significance of the mean of difference between the two related samples

A

paired t-test (difference test)

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17
Q

based on chi-square distribution

A

χ2 -test

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18
Q

used for comparing a sample variance to a theoretical population variance

A

χ2 -test

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19
Q

used for comparing a sample variance to a theoretical population variance

A

χ2 -test

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20
Q

based on F-distribution

A

F-test

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21
Q

used to compare the variance of the two-independent samples

A

F-test

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22
Q

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

A

F-test

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23
Q

used for judging the significance of multiple correlation coefficients

A

F-test

24
Q

calculated and compared with its probable value for accepting or rejecting the null hypothesis

A

F-test

25
Q

do not suppose any particular distribution and the consequential assumptions

A

NONPARAMETRIC TESTS

26
Q

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

A

NONPARAMETRIC TESTS

27
Q

When the measurements are not as accurate as is necessary for standard tests of significance

A

NONPARAMETRIC TESTS

28
Q

apply to ordinal or nominal scale data

A

NONPARAMETRIC TESTS

29
Q

if the population mean is the same as the hypothetical mean or not

A

SIGN TESTS

30
Q

non parametric analogue of t test

A

SIGN TESTS

31
Q

based on the direction of the plus or minus signs of observations in a sample and not on their numerical magnitudes

A

SIGN TESTS

32
Q

This test is an analogue of paired t test

A

Two samples sign test (paired sign test)
/ Wilcoxon signed rank sum test (paired samples)

33
Q

use this test when have two samples having paired observations

A

Two samples sign test (paired sign test)

34
Q

for one sample t-test

A

Wilcoxon signed rank sum test (single sample)

35
Q

for paired t- test

A

Wilcoxon signed rank sum test (paired samples)

36
Q

used for ordered categorical data where a numerical scale is inappropriate, but it is possible to rank the observations

A

Both Wilcoxon test

37
Q

analogue of t-test for two independent samples drawn from continuous populations

A

Mann Whitney U test

38
Q

used to judge the randomness of a sample on the basis of the order in which the observations are taken

A

One Sample Runs Test

39
Q

This is particularly true when we have little or no control over the selection of the data

A

One Sample Runs Test

40
Q

This test is analogous to the one way analysis of variance

A

Kruskal Wallis test

41
Q

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

A

Kruskal Wallis test

42
Q

distribution-free test used in testing a hypothesis concerning no difference among two sets of data

A

FISHER-IRWIN TEST

43
Q

employed to determine whether one can reasonably assume

A

FISHER-IRWIN TEST

44
Q

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

A

FISHER-IRWIN TEST

45
Q

used when the data happen to be nominal and relate to two related samples

A

MC NEMER TEST

46
Q

useful with before-after measurement of the same subjects

A

MC NEMER TEST

47
Q

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

A

Spearman’s Rank Correlation

48
Q

measure of association that is based on the ranks of the observations and not on the numerical values of the data

A

Spearman’s Rank Correlation

49
Q

measure of correlation that exists between two sets of ranks of observations and not on the numerical values of data

A

Spearman’s Rank Correlation (rank correlation coefficient)

50
Q

Test of a hypothesis concerning some single value for the given data

A

one-sample sign test

51
Q

Test of a hypothesis concerning no difference among two or more sets of data

A

two- sample sign test
Fisher-Irwin test
Rank sum test

52
Q

Non parametric
Test of a hypothesis of a relationship between variables

A

Rank correlation
Kendall’s coefficient of concordance
other tests for dependence

53
Q

Test of a hypothesis concerning variation in the given data

A

test analogous to ANOVA = Kruskal-Wallis test.

54
Q

Tests of randomness of a sample based on the theory of runs

A

one sample runs test

55
Q

Test of hypothesis to determine if categorical data shows dependency or if two classifications are independent

A

chi-square test

Research (5 decks)

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