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YEAR 2 Stats > Non parametric statistics > Flashcards

Non parametric statistics Flashcards

1
Q

What assumptions do parametric statistics make?

A

Assumptions about the population from which the sample has been drawn

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2
Q

Makes assumptions about the population from which the sample has been drawn

a. Parametric statistics
b. Non-parametric statistics

A

a. Parametric statistics

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3
Q

Do not make assumptions about the underlying population distributions

a. Parametric statistics
b. Non-parametric statistics

A

b. Non-parametric statistics

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4
Q

Known as distribution-free statistics

a. Parametric statistics
b. Non-parametric statistics

A

b. Non-parametric statistics

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5
Q

True or False?

Non-parametric tests have less power than their parametric equivalents

A

True

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6
Q

Using non-parametric tests results in higher risk of…?

a. Type II error
b. Type I error

A

a. Type II error

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7
Q

What is the non-parametric equivalent of an independent t-test?

A

Mann-Whitney U test

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8
Q

What is the non-parametric equivalent of a paired t-test?

A

Wilcoxon T Test

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9
Q

What is the non-parametric equivalent of a one-way independent ANOVA?

A

Kruskal Wallis Test

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10
Q

What is the non-parametric equivalent of a one-way repeated measures ANOVA?

A

Friedman Test

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11
Q

What is the non-parametric equivalent of a two-way independent ANOVA?

A

No non-parametric equivalent

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12
Q

What is the non-parametric equivalent of a two-way repeated measures ANOVA?

A

No non-parametric equivalent

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13
Q

What is the non-parametric equivalent of a two-way mixed ANOVA?

A

No non-parametric equivalent

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14
Q

An experiment has 1 IV with 2 levels between subjects but normality assumptions have not been met

Which statistic is used?

a. Kruskal Wallis Test
b. Friedman Test
c. Mann-Whitney U Test
d. Wilcoxon T Test
e. No non-parametric equivalent

A

c. Mann-Whitney U Test

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15
Q

An experiment has 1 IV with 2 levels within subjects but normality assumptions have not been met

Which statistic is used?

a. Kruskal Wallis Test
b. Friedman Test
c. Mann-Whitney U Test
d. Wilcoxon T Test
e. No non-parametric equivalent

A

d. Wilcoxon T Test

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16
Q

An experiment has 1 IV with more than 2 levels within subjects but normality assumptions have not been met

Which statistic is used?

a. Kruskal Wallis Test
b. Friedman Test
c. Mann-Whitney U Test
d. Wilcoxon T Test
e. No non-parametric equivalent

A

b. Friedman Test

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17
Q

An experiment has 1 IV with more than 2 levels between subjects but normality assumptions have not been met

Which statistic is used?

a. Kruskal Wallis Test
b. Friedman Test
c. Mann-Whitney U Test
d. Wilcoxon T Test
e. No non-parametric equivalent

A

a. Kruskal Wallis Test

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18
Q

An experiment has 2 IVs between subjects but normality assumptions have not been met

Which statistic is used?

a. Kruskal Wallis Test
b. Friedman Test
c. Mann-Whitney U Test
d. Wilcoxon T Test
e. No non-parametric equivalent

A

e. No non-parametric equivalent

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19
Q

An experiment has 2 IVs within subjects but normality assumptions have not been met

Which statistic is used?

a. Kruskal Wallis Test
b. Friedman Test
c. Mann-Whitney U Test
d. Wilcoxon T Test
e. No non-parametric equivalent

A

e. No non-parametric equivalent

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20
Q

An experiment has 2 IVs mixed subjects but normality assumptions have not been met

Which statistic is used?

a. Kruskal Wallis Test
b. Friedman Test
c. Mann-Whitney U Test
d. Wilcoxon T Test
e. No non-parametric equivalent

A

e. No non-parametric equivalent

21
Q

How do we check that the normality assumption is met for independent designs?

A

The DV should be normally distributed, under each
level of the IV

22
Q

How do we check that the normality assumption is met for repeated measures designs?

A

The DV difference scores should be normally
distributed, between each paired level of the IV

23
Q

The DV should be normally distributed, under each
level of the IV

a. Repeated measures
b. Independent

A

b. Independent

24
Q

The DV difference scores should be normally
distributed, between each paired level of the IV

a. Repeated measures
b. Independent

A

a. Repeated measures

25
Q

How can we assess the normality assumption statistically?

A

Using Shapiro-Wilk test

26
Q

What is the Shapiro-Wilk test for?

A

Assessing normality assumption statistically

27
Q

List 3 parametric tests of relationships

A
  1. Pearson’s correlation coefficient
  2. Partial correlation
  3. Regression
28
Q

What are the non-parametric equivalents of Pearson’s correlation coefficient?

A
  1. Spearman’s rho

Used where N > 20

  1. Kendall’s Tau

Used where N < 20

29
Q

What are the non-parametric equivalents of partial correlation?

A

There are none

30
Q

What are the non-parametric equivalents of regression?

A

There are none

31
Q

List 7 parametric tests of differences

A
  1. Independent t-test
  2. Paired t-test
  3. One-way independent ANOVA
  4. One-way repeated measures ANOVA
  5. Two-way independent ANOVA
  6. Two-way repeated measures ANOVA
  7. Two-way mixed ANOVA
32
Q

List 4 non-parametric tests of differences

A
  1. Mann-Whitney U
  2. Wilcoxon T Test
  3. Kruskal Wallis Test
  4. Friedman Test
33
Q

Spearman’s rho is the non-parametric equivalent of Pearson’s correlation coefficient for…?

a. Used where N < 20
b. Used where N > 20

A

b. Used where N > 20

34
Q

Kendall’s Tau is the non-parametric equivalent of Pearson’s correlation coefficient for…?

a. Used where N < 20
b. Used where N > 20

A

a. Used where N < 20

35
Q

Name the non-parametric equivalent for Pearson’s correlation coefficient when N < 20

A

Kendall’s Tau

36
Q

Name the non-parametric equivalent for Pearson’s correlation coefficient when N > 20

A

Spearman’s rho

37
Q

Should we use parametric or non-parametric when testing for relationships in this scenario?

Either variable is measured on an ordinal
scale

A

Non-parametric

38
Q

True or False?

It is best to use parametric if either variable is measured on an ordinal scale (especially if you are concerned that the intervals between measures
are not equivalent)

A

False

It is best to use non-parametric if either variable is measured on an ordinal scale (especially if you are concerned that the intervals between measures
are not equivalent)

39
Q

When should we use non-parametric tests of relationships other than when the normality assumption is not met?

A

If either variable is measured on an ordinal
scale (especially if you are concerned that the intervals between measures are not equivalent)

40
Q

What are the 2 tests we can run to analyse categorical data?

A
  1. One-variable Chi-Square (a.k.a. Goodness of Fit Test
  2. Chi-Square Test of Independence (two variables)
41
Q

The Chi-Square Test with two variables is known as…?

A

Chi-Square Test of Independence

42
Q

What is the Chi-Square Test of Independence?

A

Analysis of categorical data with two variables

43
Q

Analysis of categorical data with two variables

This is known as…?

A

Chi-Square Test of Independence

44
Q

The Goodness of Fit Test is also known as…?

A

One-variable Chi-Square

45
Q

What is the One-variable Chi-Square?

A

Analysis of categorical data with one variable

AKA Goodness of Fit Test

46
Q

Analysis of categorical data with one variable

AKA Goodness of Fit Test

This is known as…?

A

One-variable Chi-Square

47
Q

What is the non-parametric equivalent for One-variable Chi-Square?

A

There are none

48
Q

What is the non-parametric equivalent for the Chi-Square Test of Independence?

A

There are none

49
Q

Why are there no non-parametric equivalents for the One-variable Chi-Square (a.k.a. Goodness of Fit Test) and Chi-Square Test of Independence?

A

If the DV (outcome variable) is measured on a categorical scale, non-parametric tests are the only option

YEAR 2 Stats (10 decks)

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