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Sport & Exercise Science > SES - Descriptive Stats > Flashcards

SES - Descriptive Stats Flashcards

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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
Q

When do we use non-parametric inferential stats? Examples of what kind of data we don’t use it with?

A

Ratio & interval data set which is not normally distributed e.g. nominal data, ordinal data.

26
Q

Measures of central tendency (averages)?

A

Mean.
Median.
Mode.

27
Q

Measures of variability?

A

Range.
Interquartile range.
Standard deviation.
Coefficient of variation.

28
Q

CT?

A

Central tendency.

29
Q

IQR?

A

Interquartile range.

30
Q

SD?

A

Standard deviation.

31
Q

CV?

A

Coefficient of variation.

32
Q

How would you describe shape when summarising data and its distribution?

A

Through skewness and kurtosis.

33
Q

Most commonly used measure of central tendency?

A

Mean.

34
Q

Median?

A

Middle score.
Occurs in the middle of a list of ordered scores.
Divides data set in half.

35
Q

Mode?

A

Most common value/most frequently occurring score.

36
Q

What do measures of variability indicate?

A

Dispersion of scores in a distribution.

How widely spread scores are/how similar they are to one another.

37
Q

Examples of measures of variability?

A

Range.
IQR.
SD.
CV.

38
Q

Interquartile range?

A

Distance between the raw scores at 75th (Q3) and the 25th (Q1) percentile points.
Q3 - Q1 = IQR.

39
Q

Most frequently used measure of variability?

A

SD.

40
Q

6 steps to calculating SD?

A
  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
Q

Coefficient of variability? Formula?

A

Compares the variability of 2 different measures.

CV = (SD/X) x 100.

42
Q

What does CV allow?

A

Comparison of scores in different units.

43
Q

Measure of variability to choose for ratio/interval data that is not normally distributed & ordinal data?

A

IQR.

44
Q

Measures of variability to choose for ratio/interval data that is normal or near normal?

A

Range.
SD.
CV.

45
Q

Negatively skewed graph?

A

Sloping upwards towards the right side of the graph.

46
Q

Normal/no skew graph?

A

Normal curve.

47
Q

Positively skewed graph?

A

Sloping upwards towards the left side of the graph.

48
Q

What is acceptable skewness?

A

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
Q

Leptokurtic?

A

Thin + positive line

50
Q

Platykurtic?

A

Flat + negative line

51
Q

What is normal distribution in relation to kurtosis on a graph? Non-normal?

A

Standardised kurtosis smaller +/- 2 SD.

Standardised kurtosis greater +/- 2 SD.

52
Q

When to choose range for ratio/interval data that is normal or near normal?

A

If a quick estimate is needed.

53
Q

When to choose standard deviation for ratio/interval data that is normal/near normal?

A

When a precise indicator is needed.

54
Q

When to choose coefficient of variability for ratio/interval data that is normal or near normal?

A

When a comparison between different measures are needed.

55
Q

What does quantitative data, such as nominal, ordinal, interval & ratio data decide?

A

Which statistical test you use.

56
Q

What does ordinal data reflect?

A

The order e.g. 1st, 2nd, 3rd etc.

57
Q

What are measures of central tendency?

A

Different types of averages e.g. mean, median, mode.

58
Q

When to choose the mean as a measure of central tendency?

A

With ratio/interval data where the distribution is normal.

59
Q

When to choose the median as a measure of central tendency?

A

With ordinal data.

With ratio/interval data where the distribution is not normal.

60
Q

When to choose the mode as a measure of central tendency?

A

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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