PARAMETRIC AND NON PARAMETRIC TESTS Flashcards

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1
Q

What are the four major parametric tets?

A

Z test, T test, ANOVA and Two way ANOVA

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2
Q

A tets used when the population is greater than 30

A

Z test

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3
Q

A test when the population is greater than 10

A

T test

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4
Q

A test determining the significant differences between means of three or more independent variables

A

One way ANOVA

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5
Q

A test compares the mean differfences between groups that have been SPLIT into two independent variables (called factors) on the dependent variable

A

Two way ANOVA

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6
Q

What are the situations which you are required to use parametric tests?

A
  1. If the data involves nominal and ordinal measurement.
  2. Data do not satisfy assumptions underlying parametric tests
  3. If the variance is extremely high
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7
Q

What are the three major examples of nonparametric tests?

A

Chi square, Binomial test and Sign tests

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8
Q

A nonparemetric test used to know the difference of the distribution of the categorical variables from one another

A

Chi square

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9
Q

Two kinds of Chi Square test

A
  1. Chi square for independence

2. Chi square goodness of fit

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10
Q

A type of chi square which u want to know if there is a significant difference of the two categorical variables from a single population

A

Chi square for independence

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11
Q

A type of chi square which u want to know if one sample data is consistent from a distribution of categorical variables

A

Chi square goodness of fit

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12
Q

It is a nonparemetric test which is an exact test of statistical significance of deviation from a thereotically expected distribution of two categories

A

Binomial Test

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13
Q

It is a parametrical test which is the alternative test to Wilcoxon test for dependent data

A

Sign Test

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14
Q

A data needs to be least interval scaled

A

Wilcoxon test

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15
Q

A data needs to be least ordina scaled

A

Sign Test

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16
Q

PARAMETRIC VS. NONPARAMETRIC

1. In terms of assumed distribution

A

PARAMETRIC - Normal

NONPARAMETRIC - Any

17
Q

PARAMETRIC VS. NONPARAMETRIC

2. In terms of assumed variance

A

PARAMETRIC - Homogenous

NONPARAMETRIC - Any

18
Q

PARAMETRIC VS. NONPARAMETRIC

3. Typical data

A

PARAMETRIC - Ratio or Interval

NONPARAMETRIC - Ordinal or Nominal

19
Q

PARAMETRIC VS. NONPARAMETRIC

4. Data set relationship

A

PARAMETRIC - Independent

NONPARAMETRIC - Any

20
Q

PARAMETRIC VS. NONPARAMETRIC

5. Usual central measure

A

PARAMETRIC - Mean

NONPARAMETRIC - Median

21
Q

PARAMETRIC VS. NONPARAMETRIC

6. Benefits

A

PARAMETRIC - Can draw more conclusions

NONPARAMETRIC - Less affected by outliers

22
Q

PARAMETRIC VS. NONPARAMETRIC

1. Choosing

A

PARAMETRIC - Choosing parametric test

NONPARAMETRIC -Choosing non-parametric test

23
Q

PARAMETRIC VS. NONPARAMETRIC

2. Correlation Test

A

PARAMETRIC - Pearson

NONPARAMETRIC - Spearman

24
Q

PARAMETRIC VS. NONPARAMETRIC

3. Independence measures, 2 groups

A

PARAMETRIC - Independence measures T Test

NONPARAMETRIC - Mann-Whitney Test

25
Q

PARAMETRIC VS. NONPARAMETRIC

4. Independence measures, >2 groups

A

PARAMETRIC - One way ANOVA

NONPARAMETRIC - kruskal Wallis Test

26
Q

PARAMETRIC VS. NONPARAMETRIC

5. Repeated measures, 2 conditions

A

PARAMETRIC - Matched paired

NONPARAMETRIC - Wilcoxon Test

27
Q

PARAMETRIC VS. NONPARAMETRIC

5. Repeated measures, >2 conditions

A

PARAMETRIC - Oneway ANOVA

NONPARAMETRIC - Friedman’s Test