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Research And Study Skills (Y1) > Descriptive Statistis > Flashcards

Descriptive Statistis Flashcards

1
Q

What are the three branches of statistics

A
  1. Descriptive statistics (organising, summarising and describing data)
  2. Correlational (exploring relationships)
  3. Inferential (generalising findings)
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2
Q

State what it is meant by the key term ‘variables’

A

Variables are measurements made within a study

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3
Q

What are the 4 types of variables (on the chart, in order)

A
  1. Organismic (top)
  2. Environmental (bottom)
  3. Discrete (left)
  4. Continuous (right)
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4
Q

Explain what it is meant by organismic (top) variables

A

Any measurement that can describe a characteristic of an organism

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5
Q

Explain what it is meant by environmental variables

A

Any measurement to describe an organisms surroundings

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6
Q

State 2 facts to explain what it is meant by discrete variables

A
  1. Any parameters that can only take one score

2. Can’t be sub-divided

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7
Q

Explain what it is meant by continuous variables

A

Measurements that can always be sub-divided more

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8
Q

State, and give an example, of all the combinations that can be made on the variable graph

A
  1. Discrete/organismic - biological sex
  2. Discrete/environmental - treatment used in the experiment
  3. Continuous/organismic - body weight
  4. Continuous/environmental - temperature
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9
Q

State, from bottom to top, the levels of measurement (LoM)

A
  1. Nominal data
  2. Ordinal data
  3. Interval data
  4. Ratio data
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10
Q

Explain nominal data (3 points)

A
  1. About identity/categories (eg - male vs female)
  2. Can’t infer order of magnitude
  3. Mutually exclusive and comprehensive
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11
Q

Explain ordinal data (3 points)

A
  1. Can infer order (eg - small to large)
  2. Don’t know the absolute magnitude of each data sample
    • all the aspects of nominal data
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12
Q

Explain interval data (4 points) - example being shoe sizes

A
  1. Can include difference between categories
  2. Know the absolute score of each category
  3. Can’t say A is x times bigger than B
    • everything in interval and nominal data
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13
Q

Explain ratio data (2 points) - example being 100m sprint

A
  1. Can express differences between data sets as magnitudes, percentages, fold changes, etc…
    • everything in interval, ordinal and interval level data
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14
Q

State 2 things should should always/never do when using units

A
  1. Always - leave a space between the measurement and the unit
  2. Never - pluralise (add an s on) or italicise (write in italics)
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15
Q

State 2 positives and 1 negative of using the Mode

A
  1. Extreme outliers don’t impact
  2. Used with any LoM
  3. It’s a terminal statistic, all it shows is the mode
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16
Q

State 2 positives and one negative of using the Median

A
  1. Extreme outliers have no effect
  2. Any LoM above nominal data
  3. Ignores values - only using the median
17
Q

State 1 positive and one negative of using the Mean

A
  1. Takes into account and the weight of each measurement

2. Can only be used with interval and ratio LoM’s

18
Q

State what it is meant by the key term ‘asymptotic’ when talking about distribution

A

If a distribution curve is asymptotic, it means that the distribution curve never hits zero

19
Q

State what it is meant by ‘symmetrical’ when talking about distribution curves

A

If a distribution curve is symmetrical, then it means the mode, median and the mean are all in the same place on the curve

20
Q

State what it is meant by the ‘point of inflation’ on a distribution curve

A
  1. Where curve changes from convex to concave
  2. Shows the SD
  3. 80% should be within 1SD of the mean
21
Q

State what it is meant by the key term ‘Kurtosis’

A

Kurtosis refers to how we size of the SD: mesokurtic, leptokurtic and platykurtic

22
Q

Explain the three types of Kurtosis

A
  1. Mesokurtic - usual SD
  2. Leptokurtic - very thin SD
  3. Platykurtic - very wide/varied SD
23
Q

State what it is meant by the key term ‘Z score’

A

The Z score is the number of SD’s you are away from the mean

24
Q

What are the two types of skew

A
  1. Positive skew - on the right of the curve

2. Negative skew - on the left of the curve

25
Q

State 2 facts about using ‘mean +/- SD’

A
  1. Requires normal distribution

2. SD represents how spread out data is around the mean

26
Q

State 3 facts about using ‘mean +/- SEM’

A
  1. 68% certain mean of target pop. lies within difference of out mean
  2. SEM measures how accurate out mean is
  3. Never report SEM with method data
27
Q

State 2 facts about using ‘mean +/- nCL’ (normalised confidence intervals)

A
  1. Tells you where 95% of your measurements lie

2. Tells you the difference between groups, if groups don’t cross then it’s statistically significant

Research And Study Skills (Y1) (4 decks)

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