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Sport & Exercise Science > SES - Descriptive Stats > Flashcards

SES - Descriptive Stats Flashcards

1
Q

Mean?

A

Arithmetic average.

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2
Q

Standard deviation?

A

Average amount that each score varies from the mean.

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3
Q

Nominal data?

A

Scores/people are separated into categories.

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4
Q

Normally distributed data is also known as what?

A

Mesokurtic.

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5
Q

When do people use the median as a measure of central tendency?

A

With ratio/interval data that is normally/near normally distributed.

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6
Q

When do we use parametric inferential stats?

A

With ratio & interval data that is normally distributed.

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7
Q

Qualitative data?

A

Any info that is non-numerical.

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8
Q

Quantitative data?

A

Numerical data.

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9
Q

Types of quantitative data?

A

NOIR:

  • Nominal.
  • Ordinal.
  • Interval.
  • Ratio.
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10
Q

What does nominal (categorical) data seperate?

A

Scores/people into categories.

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11
Q

What is the number of scores/people within each category of a set of nominal data called?

A

Frequency.

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12
Q

Examples of nominal data?

A

Hair colour.

Gender.

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13
Q

What does ordinal data rank?

A

Scores/people in order.

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14
Q

Examples of ordinal data?

A

The finishing places of runners in London marathon.

Order of height of each student in class.

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15
Q

Interval data?

A

Measurement units/intervals are equal distance apart.
No true zero point.
Negative values.

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16
Q

Example of interval data?

A

Celsius temp scale.

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17
Q

Ratio data?

A

Measurement units/intervals equal distance apart.
Zero = no value at all.
No negative values.

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18
Q

Example of ratio data?

A

Kelvin temp scale.

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19
Q

2 types of inferential stats?

A

Parametric tests.

Non-parametric tests.

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20
Q

Examples of parametric tests that test for differences?

A

t-test.

ANOVA.

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21
Q

Examples of parametric tests that test for relationships?

A

Correlations.

Regressions.

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22
Q

Examples of non-parametric tests that test for differences?

A

Wilcoxon test/Mann-Whitney.

Freedman/Kruskal-Wallis.

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23
Q

Examples of non-parametric tests that test for relationships?

A

Spearman rank correlation.

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24
Q

What do parametric stats assume?

A

Population is normally distributed and therefore a measured sample will reflect the population, with a known probability.

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25
When do we use non-parametric inferential stats? Examples of what kind of data we don't use it with?
Ratio & interval data set which is not normally distributed e.g. nominal data, ordinal data.
26
Measures of central tendency (averages)?
Mean. Median. Mode.
27
Measures of variability?
Range. Interquartile range. Standard deviation. Coefficient of variation.
28
CT?
Central tendency.
29
IQR?
Interquartile range.
30
SD?
Standard deviation.
31
CV?
Coefficient of variation.
32
How would you describe shape when summarising data and its distribution?
Through skewness and kurtosis.
33
Most commonly used measure of central tendency?
Mean.
34
Median?
Middle score. Occurs in the middle of a list of ordered scores. Divides data set in half.
35
Mode?
Most common value/most frequently occurring score.
36
What do measures of variability indicate?
Dispersion of scores in a distribution. | How widely spread scores are/how similar they are to one another.
37
Examples of measures of variability?
Range. IQR. SD. CV.
38
Interquartile range?
Distance between the raw scores at 75th (Q3) and the 25th (Q1) percentile points. Q3 - Q1 = IQR.
39
Most frequently used measure of variability?
SD.
40
6 steps to calculating SD?
1. ) Calculate mean. 2. ) Calculate how each data point deviates from the mean. 3. ) Square these deviations. 4. ) Sum the squared deviations. 5. ) Divide this figure by the number of scores in the group. (zero = not a score) 6. ) Square root this figure.
41
Coefficient of variability? Formula?
Compares the variability of 2 different measures. | CV = (SD/X) x 100.
42
What does CV allow?
Comparison of scores in different units.
43
Measure of variability to choose for ratio/interval data that is not normally distributed & ordinal data?
IQR.
44
Measures of variability to choose for ratio/interval data that is normal or near normal?
Range. SD. CV.
45
Negatively skewed graph?
Sloping upwards towards the right side of the graph.
46
Normal/no skew graph?
Normal curve.
47
Positively skewed graph?
Sloping upwards towards the left side of the graph.
48
What is acceptable skewness?
Small deviations are acceptable. Data set is normal when standardised skewness is smaller than +/- 2 SD. Data is not normal when standardised skewness is greater than +/- 2 SD.
49
Leptokurtic?
Thin + positive line
50
Platykurtic?
Flat + negative line
51
What is normal distribution in relation to kurtosis on a graph? Non-normal?
Standardised kurtosis smaller +/- 2 SD. | Standardised kurtosis greater +/- 2 SD.
52
When to choose range for ratio/interval data that is normal or near normal?
If a quick estimate is needed.
53
When to choose standard deviation for ratio/interval data that is normal/near normal?
When a precise indicator is needed.
54
When to choose coefficient of variability for ratio/interval data that is normal or near normal?
When a comparison between different measures are needed.
55
What does quantitative data, such as nominal, ordinal, interval & ratio data decide?
Which statistical test you use.
56
What does ordinal data reflect?
The order e.g. 1st, 2nd, 3rd etc.
57
What are measures of central tendency?
Different types of averages e.g. mean, median, mode.
58
When to choose the mean as a measure of central tendency?
With ratio/interval data where the distribution is normal.
59
When to choose the median as a measure of central tendency?
With ordinal data. | With ratio/interval data where the distribution is not normal.
60
When to choose the mode as a measure of central tendency?
With nominal data. | With ratio/interval data that is normal and only a rough estimate of CT is needed.

Sport & Exercise Science (33 decks)

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