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BioStats > Parametric and Non-Parametric Tests > Flashcards

Parametric and Non-Parametric Tests Flashcards

1
Q

What is a parametric test?

A

They assume that the data follows a specific distribution, typically normal.

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2
Q

When should you use a non-parametric test?

A

When data do not meet the assumptions necessary for parametric tests, or when dealing with ordinal data or ranks.

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3
Q

What assumptions must be met for a t-test?

A

Normality of data, homogeneity of variances, and independence of observations.

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4
Q

Describe the ANOVA test and its purpose.

A

It compares means across multiple groups to determine if at least one differs significantly.

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5
Q

How do you interpret the results of a Chi-square test?

A

It assesses whether observed frequencies differ significantly from expected frequencies.

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6
Q

What is the Mann-Whitney U test used for?

A

It compares differences between two independent samples using ranks.

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7
Q

Explain the purpose of the Wilcoxon signed-rank test.

A

Used to compare two related samples where the data are not normally distributed.

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8
Q

What differences are there between the t-test and the Mann-Whitney U test?

A

The t-test assumes normality and equal variances; the Mann-Whitney does not.

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9
Q

What is the significance of homogeneity of variances in ANOVA?

A

It is crucial because unequal variances can lead to erroneous conclusions in ANOVA.

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10
Q

How can you test for normality before conducting a parametric test?

A

Using graphical plots like Q-Q plots, or tests like the Shapiro-Wilk test.

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11
Q

What are the key advantages of using non-parametric tests?

A

They are more flexible and can be used with ordinal data or non-normal distributions.

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12
Q

How does sample size affect the choice between parametric and non-parametric tests?

A

Non-parametric tests are more suitable for small sample sizes or when assumptions are not met.

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13
Q

What is the Kruskal-Wallis test?

A

It compares the medians of three or more independent groups.

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14
Q

How do you determine which type of ANOVA to use?

A

Based on the number of factors and the independence of samples.

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15
Q

What are the assumptions behind the Pearson correlation coefficient?

A

The data must be normally distributed and the relationship between variables linear.

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16
Q

Why is the Spearman’s rank correlation coefficient considered a non-parametric test?

A

It does not assume normality and works with rank-ordered data.

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17
Q

What statistical test would you use to compare the means of three or more groups?

A

ANOVA is typically used for this purpose.

18
Q

What is the best test to use when comparing two related samples?

A

The paired t-test for parametric data, or the Wilcoxon signed-rank test for non-parametric data.

19
Q

Why might parametric tests be more powerful than non-parametric tests?

A

Because they make more assumptions about the data, which can provide more precise results if those assumptions are met.

20
Q

What are the limitations of non-parametric tests?

A

They often have less statistical power and may not provide as detailed information about the population.

21
Q

How do you handle violations of assumptions in parametric tests?

A

Adjust the analysis method or transform the data to better meet the assumptions.

22
Q

Why is it important to check for outliers before conducting a parametric test?

A

Outliers can significantly skew the results and violate the assumptions of normality and homogeneity.

23
Q

What role does independence of observations play in statistical testing?

A

It ensures that the test results are valid and that the samples do not influence each other.

24
Q

How do you interpret a significant result in a non-parametric test?

A

It indicates that there is a statistically significant difference between the groups or conditions tested.

25
Q

What adjustments should be made for multiple comparisons in ANOVA?

A

Bonferroni correction or other methods to adjust for the increased risk of Type I errors.

26
Q

How do you choose between a one-way and a two-way ANOVA?

A

Choose one-way for one independent variable and two-way for two independent variables.

27
Q

What are the benefits of using the Wilcoxon signed-rank test over the paired t-test?

A

It does not assume normal distribution of differences and is less affected by outliers.

28
Q

What methods can be used to assess the effect size in non-parametric tests?

A

Using rank-based methods or estimating the interquartile range difference.

29
Q

How do you handle data that does not meet the assumptions of homoscedasticity?

A

Using robust statistical methods or non-parametric tests.

30
Q

What are common errors in the application of Chi-square tests?

A

Not meeting the assumption of expected frequency counts being high enough in each category.

31
Q

What should you consider when choosing between a parametric and a non-parametric correlation coefficient?

A

Consider the distribution of the data and whether the data meets the assumptions of the parametric test.

32
Q

How do bootstrapping methods benefit non-parametric tests?

A

They allow for estimating distributions from the data without assuming a specific form.

33
Q

What is the Friedman test and when should it be used?

A

Used for comparing more than two groups that are related, measured on at least an ordinal scale.

34
Q

Can non-parametric tests be used on ordinal data?

A

Yes, they are especially suitable for ordinal data.

35
Q

How do you ensure the reliability of test results in non-parametric tests?

A

By using appropriate sample sizes and ensuring the test assumptions are adequately met.

36
Q

What are the consequences of using the wrong type of statistical test?

A

It can lead to incorrect conclusions, affecting the validity and reliability of the research findings.

37
Q

Why is it necessary to use a control group in experimental designs using ANOVA?

A

To provide a baseline against which the treatment effects can be compared.

38
Q

What are the implications of a low power in non-parametric tests?

A

It may fail to detect actual effects or differences when they exist.

39
Q

How can transformations improve the applicability of parametric tests?

A

Making data more normal, thus meeting the assumptions of parametric tests more closely.

40
Q

What should be considered when interpreting the results of statistical tests?

A

Ensure that all test assumptions have been met and consider the practical significance of the results.

BioStats (14 decks)

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