PSY201: Chapter 4 - Variability Flashcards
Variability
distribution only partially described through a measure of central tendency
describe distributions in terms of central tendency + variability
Variability
describe how much scores differ from that average
Variability
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
Central Tendency
describes central point of the distribution
variability describes how scores are scattered around that central point
Central Tendency and Variability
2 primary values used to describe distribution of scores
Variability
distributions differ from each other in terms of how much scores deviating from mean
Variability
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.
Variability
both descriptive measure + important component of most inferential statistics
descriptive statistic - measures degree to which scores are spread out/clustered together in a distribution
Variability
inferential statistics - measure of how accurately any individual score/sample represents the entire population.
Variability
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
Measuring Variability
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
Range
distance from largest-smallest score in distribution
defined in terms of distance - interval/ratio scale measurements of continuous variable
Range
take diff betw upper real limit of largest X + lower real limit of smallest X value
Range= URLXmax–LRLXmin
Range
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
Interquartile Range
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
Interquartile Range
figure out how many scores represent 1⁄4 of the data
refer to range of middle 2 quarters
Interquartile range = Q3 - Q1
Interquartile Range
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
Interquartile Range
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
Standard Deviation and Variance for a Population
better measure - considers distance of each score
want to measure standard/typical distance from the mean
Standard Deviation and Variance for a Population
Deviation score = X − μ
Standard Deviation and Variance for a Population
sign - direction of value from mean (above/below)
Standard Deviation and Variance for a Population
Because deviation scores built about mean - must sum to zero
Sum of deviations = Σ(X - μ)
Standard Deviation and Variance for a Population
avg deviation of scores around mean always zero
meaningless measure for variability
Standard Deviation and Variance for a Population
Square diff (deviation) before calculating sum of deviations, - sum of squares take into account magnitude but not direction of the difference from the means