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Flashcards in eM2 – Choosing statistics Deck (33)
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1
Q

In terms of analysis, what are correlations?

A

Hypothesis tests to evaluate relationships between variables

2
Q

What are comparisons?

A

Hypothesis tests to evaluate differences between groups or populations

3
Q

What is quantitative data?

A

Numeric information about quantities - i.e height width etc.

4
Q

What is qualitative / categorical data?

A

Information that cannot be measured - i.e. gender, stages of disease etc.

5
Q

Give two types of quantitative data and an example for each:

A

Measured (continuous): age

Counted (discrete): number of people with hypertension

6
Q

Give two types of qualitative/categorical data and an example for each:

A

Nominal: Gender, Colour

Ordinal: Fitness (not fit, quite fit, very fit), Happiness

7
Q

What is the difference between continuous and discrete data?

A

Continuous can be divided to finer and more precise levels.

Discrete data cannot be made more precise.

8
Q

What is nominal data?

A

Qualitative data containing individual categories that cannot be put in an implicit rank/order

9
Q

What is ordinal data?

A

Categories that have an implicit/natural order.

10
Q

What is normality in terms of statistical analysis?

A

Normality is a measure of central tendency and dispersion of data - i.e symmetric distribution with “well behaved tails”

11
Q

What is meant by left skewness?

A

Mean to the left of the peak, long tail in negative (decreasing) direction of curve

12
Q

What is meant by right skewness?

A

Mean to the right of the peak, long tail in positive (increasing) direction of the curve

13
Q

What is kurtosis?

A

The sharpness of a peak of a distribution curve

14
Q

What two factors do statistical tests rely on?

A

50% of values above and below mean - symmetrical

2/3rds of data within 1 SD from mean - normal distribution

15
Q

How to assess normality of data quantitatively?

A

Shapiro-Wilks test - n<50

Kolmogarov-Smirnof test - n>50

16
Q

What is descriptive statistics?

A

A method of categorising large data sets into a format easy to read (tangible).

17
Q

What is the mean?

A

μ = ( Σ Xi ) / N

18
Q

What is the median?

A

(n+1)/2 -th number in the data set.

19
Q

What is the mode?

A

Most frequent data entry.

20
Q

What is the standard deviation in a data set?

A

σ = sqrt[ Σ ( Xi – μ )^2 / N ]

A measure of how dispersed the data are from the mean.

21
Q

What is meant by dependant (paired) data?

A

When the data being collected is consistantly being collected from the same subject

22
Q

What is meant by parametric statistics?

A

When the data from the population are well described by the mean and SD - normally distributed.

23
Q

What is meant by non-parametric statistics?

A

When the data is not well described by the mean - non-normally distributed quantitative data.

note: non-parametric tests are used for qualitative data.

24
Q

Parametric, 2 groups, paired

A

Paired t-test

25
Q

Parametric, 2 groups, unpaired

A

Independant t-test

26
Q

Parametric, 3+ groups, paired

A

Repeated measures, one way ANOVA

27
Q

Parametric, 3+ groups, unpaired

A

one way ANOVA

28
Q

Non-parametric, 2 groups, paired

A

Wilcoxon Signed Rank test

29
Q

Non-parametric, 2 groups, unpaired

A

Mann-Whitney U test

30
Q

Non-parametric, 3+ groups, paired

A

Friedman test

31
Q

Non-parametric, 3+ groups, unpaired

A

Kruskall-Wallis test

32
Q

To test for a linear relationship in a normally distributed population:

A

Pearson’s Correlation test

33
Q

To test for a linear relationship in a non-normally distributed population:

A

Spearman’s Correlation test