PSY201: Chapter 4 - Variability Flashcards

1
Q

Variability

A

distribution only partially described through a measure of central tendency
describe distributions in terms of central tendency + variability

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2
Q

Variability

A

describe how much scores differ from that average

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3
Q

Variability

A

to obtain a measure of how spread out the scores are in a distribution
usually accompanies measure of central tendency as basic descriptive statistics for a set of scores

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4
Q

Central Tendency

A

describes central point of the distribution

variability describes how scores are scattered around that central point

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5
Q

Central Tendency and Variability

A

2 primary values used to describe distribution of scores

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6
Q

Variability

A

distributions differ from each other in terms of how much scores deviating from mean

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7
Q

Variability

A

shows how well an individual score represents the entire
distribution
how much error to expect - important for making conclusions from small samples using inferential statistics.

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8
Q

Variability

A

both descriptive measure + important component of most inferential statistics
descriptive statistic - measures degree to which scores are spread out/clustered together in a distribution

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9
Q

Variability

A

inferential statistics - measure of how accurately any individual score/sample represents the entire population.

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10
Q

Variability

A

pop variability small ⇒ scores clustered close together + individual score/sample will provide good representation of entire set
variability large ⇒ scores widely spread, easy for 1/2 extreme scores to give distorted picture of general pop

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11
Q

Measuring Variability

A

Range: Diff betw highest + lowest score
Interquartile range: Point of the 25th percentile + point of 75th percentile.
Standard Deviation/Variance: Avg squared distance from mean - most important variability measure
variability determined by measuring distance

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12
Q

Range

A

distance from largest-smallest score in distribution

defined in terms of distance - interval/ratio scale measurements of continuous variable

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13
Q

Range

A

take diff betw upper real limit of largest X + lower real limit of smallest X value
Range= URLXmax–LRLXmin

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14
Q

Range

A

simple way to describe spread of scores
completely dependent on max + min scores
Outliers can have huge influence on this measure of dispersion
range considered to be least important measure of variability

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15
Q

Interquartile Range

A

avoid being influenced by extreme, potentially unrepresentative, scores
distance covered by middle 50% of distribution
25th, 50th + 75th %iles are quartiles” because they cut the sample into four equal parts.
= Q3 – Q1

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16
Q

Interquartile Range

A

figure out how many scores represent 1⁄4 of the data
refer to range of middle 2 quarters
Interquartile range = Q3 - Q1

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17
Q

Interquartile Range

A

semi-interquartile range: half of the interquartile range

measures distance from middle of the distribution to boundaries that define the middle 50% = (Q3−Q1)/2

18
Q

Interquartile Range

A

more stable than the range - not influenced by outliers
disadvantage - using 50% of scores leaves out much of data doesn’t give complete pic of variability
considered to be a crude reduction of the data

19
Q

Standard Deviation and Variance for a Population

A

better measure - considers distance of each score

want to measure standard/typical distance from the mean

20
Q

Standard Deviation and Variance for a Population

A

Deviation score = X − μ

21
Q

Standard Deviation and Variance for a Population

A

sign - direction of value from mean (above/below)

22
Q

Standard Deviation and Variance for a Population

A

Because deviation scores built about mean - must sum to zero

Sum of deviations = Σ(X - μ)

23
Q

Standard Deviation and Variance for a Population

A

avg deviation of scores around mean always zero

meaningless measure for variability

24
Q

Standard Deviation and Variance for a Population

A
Square diff (deviation) before calculating sum of deviations, - sum of squares
take into account magnitude but not direction of the difference from the means
25
Population variance
mean of the squared deviations | avg of squared distances from the mean (sum of squares)
26
Standard Deviation
we correct for squaring the means by taking the square root of variance =√variance
27
Standard Deviation
square root of the avg squared deviation | avg distancefromthemean.
28
Standard Deviation
we cannot compute for nominal/ordinal scales
29
sum of squared deviations
SS=∑(X−μ)2 Find each deviation score Square each deviation score + Add squared deviations
30
Variance
σ^2 = SS/N | mean squared deviation
31
Standard Deviation
σ = square root of mean squared deviations √SS/N estimate of average deviation σ = √∑(X−μ)2/N
32
Sample Variance and Standard Deviation
need to estimate population parameters from sample samples statistics give biased estimations of pop parameters underestimate pop variance
33
Sample Variance and Standard Deviation
∑(X−M)2 Computational Formula: SS= ∑(X)^2-(∑X)2/n s^2 = ∑(X−M)2/n-1 s=√∑(X−M)2/n-1
34
Summary of Computing Standard Deviations
Compute deviation (distance from the mean) for each score. Square each deviation. Compute mean of the squared deviations. sum the squared deviations (SS) + divide by N
35
Summary of Computing Standard Deviations
divide the sum of the squared deviations (SS) by n - 1, rather than N. n - 1 - df: sample variance will provide an unbiased estimate of the pop variance square root of variance to obtain the standard deviation.
36
Degrees of freedom (df) and bias
represents # scores in sample independent + free to vary | We estimate pop mean with sample mean, M.
37
Sample Variance and Standard Deviation
apply corrections to formulas using sample data ⇒ unbiased estimates of pop variance ag of all possible sample variances pop will produce accurate estimate of pop variance
38
More About Variance and Standard | Deviation
68% of scores lie within one standard deviation of the mean | 95% within two standard deviations
39
Transformations of Scale
add constant to each score - value of mean changes | each score still same distance away from mean
40
Transformations of Scale
adding constant will move each score so entire distribution is shifted to a new location centre of the distribution (the mean) changes, but standard deviation remains the same
41
Transformations of Scale
Multiplying by constant will multiply distance betw scores | standard deviation measure of distance so it will also be multiplied