Measurements and Descriptive Stats Flashcards

1
Q

Define ‘population’

A
  • every member with selected characteristic
  • e.g. humans born in UK
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2
Q

Define ‘sample’

A
  • subset of given sample which represents the population
  • unrelated
  • chosen at random
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3
Q

Define ‘variable’

A
  • any characteristic or property that can take one of a range of values
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4
Q

Define ‘parameter’

A
  • numerical constant in any particular instance
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5
Q

Define ‘data’

A
  • refers to items of information
  • singular = datum, or data value
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6
Q

Name the 3 types of data

A
  • quantitative
  • ranked
  • qualitative
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7
Q

Define ‘quantitative data’

A
  • characteristics whose differing states can be described by ‘real’ numbers
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8
Q

Define ‘ranked data’

A
  • ordinal scale, ranked in order of magnitude
  • e.g. order of birth of children in a family
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9
Q

Define ‘qualitative data’

A
  • categorical; not measured against numerical scale nor ranked
  • non numerical and descriptive
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10
Q

Name the 3 types of quantitative data

A
  • continuous
  • discontinuous
  • derived data
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11
Q

Define ‘continuous data’

A
  • obtained by measurement
  • usually measured against numerical scale
  • significant figures/decimal places
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12
Q

Define ‘discontinuous data’

A
  • obtained by counting
  • data must be whole numbers
  • e.g. number of colonies on Petri dish
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13
Q

Define ‘derived data’

A
  • calculated from direct measurements
  • e.g. ratios, percentages, rates etc.
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14
Q

Name 4 types of measurement scales

A
  • nominal
  • ordinal
  • interval
  • ratio
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15
Q

What is a nominal scale?

A
  • classifies objects into categories based on descriptive characteristic
  • only scale suitable for qualitative data
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16
Q

What statistics are used with a nominal scale?

A
  • only those based on frequency of counts made: contingency tables, frequency distributions etc.
  • Chi-squared test
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17
Q

What is an ordinal scale?

A
  • classifies by rank
  • used with ranked data
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18
Q

What statistics are used with ordinal scales?

A
  • non-parametric methods, sign tests
  • Mann-Whitney U-test
19
Q

What is an interval scale?

A
  • numbers on equal-unit scale are related to arbitrary zero point
  • used for quantitative data
20
Q

What statistics are used with interval scales?

A
  • almost all types of test; t-test, analysis of variance (ANOVA) etc.
21
Q

What is a ratio scale?

A
  • similar to interval scale, except that the zero point now represents an absence of that character (i.e. it is an absolute zero)
22
Q

What statistics are used with ratio scales?

A
  • almost all types of test; t-test, ANOVA etc.
23
Q

Define ‘accuracy’

A
  • closeness of measurements to true value
24
Q

Define ‘precision’

A
  • closeness of repeated measurements to each other
25
Define 'bias'
- consistent non-random divergence from accuracy - can be subjective, personal, or from incorrectly calibrated instruments
26
Define 'mean'
- average value of data - obtained from sum of all data values divided by number of observations
27
Advantages and disadvantages of the mean
Advantages - good measure of centre of symmetrical frequency distributions - uses all of the numerical values of sample, therefore incorporates all information content of data Disadvantages - value of mean is greatly affected by presence of outliers (values much smaller or bigger than most data)
28
Define 'median'
- mid-point of observations when ranked in increasing order - represents location of main body of data better than mean when distribution is asymmetric or when there's outliers in sample
29
Define 'mode'
- most common value in sample - provides rapidly and easily found estimate of sample location, unaffected by outliers - however is affected by chance variation in shape of samples distribution, may lie distant from obvious centre of distribution
30
Define 'range'
- difference between largest and smallest data values in sample
31
Advantages and disadvantages of range
Advantages - easy to determine Disadvantages - greatly affected by outliers, makes it a poor measure of dispersion
32
Steps in calculating a semi-interquartile range for a data set
- rank observations in ascending order - find values of 1st and 3rd quartiles - subtract value of 1st quartile from 3rd quartile - halve this number
33
Advantages and disadvantages of semi-interquartile range
Advantages - appropriate measure of dispersion with the median being the appropriate stat to describe location Disadvantages - can only be estimated for data grouped in classes - takes no account of distribution shape at its edges -
34
What is the 'five-number summary'?
- consists of 3 quartiles and 2 extreme values; commonly presented as box-and-whisker plot - upper extreme, upper quartile, median, lower quartile, lower extreme
35
Define 'sample variance'
- sum of squared deviations of each data value from the mean divided by n - 1 (where n is sample size)
36
Define 'standard deviation'
- positive square root of sample variance - SD=√ (Σ(X – mean) 2) ÷ (n - 1)
37
Define 'coefficient of variance' (CV or CoV)
-dimensionless measure of dispersion - expresses SD as a percentage of sample mean - (SD ÷ mean) x 100 - e.g. mean = 5; SD = 2; CoV = (2÷5) x 100 = 40%
38
Define 'unimodal distribution'
- one peak - may be symmetrical or asymmetrical
39
Define 'bimodal distribution'
- two peaks (two unimodal distributions - 2 populations are being sampled
40
Define 'polymodal distribution'
- more than two peaks/unimodal distributions - more than two populations being sampled
41
Define 'positive skewness'
- longer tail of distribution occurs for higher values of measured variable
42
Define 'negative skewness'
- longer tail occurs for lower values
43
Define 'kurtosis'
- name given to pointedness of frequency distribution
44
Name the 2 types of kurtosis and what they mean
- platykurtic; flattened peak - leptokurtic; pointed peak