PSYCH 100A Lecture 4: Lecture 4

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Published on 28 Mar 2018
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Lecture 4
Symmetric Distribution
Perfect symmetric bell curves
Extreme scores in both tails have a canceling effect on the mean, located at the center of
the distribution
o Mean = median = mode
Positively skewed distribution
o Outlier scores on the high side of the distribution pull the mean toward the extreme
values
o Mode ignores the outliers, picks the most common score
o Median also ignores the outliers, picks the middle score
o Mean accounts for the outliers - everyone in the sample has an impact on the mean
Negatively skewed distribution
o Outlier scores on the low side of the score range pull the mean down toward the
extreme
Comparing distributions
Psychology research studies examine whether systematic differences exist among two or
more groups
o Examining and comparing means if a common way to evaluate group differences
how dispersed people are in the variable on the score scale
Conveys the idea that two or more things differ
Variability is an important concept in statistics
o Refers to the degree of similarity/dissimilarity to a set of scores
Ex) using the center of town to create data in numbers
Average distances, find the mean
Deviation Scores - d for distance
Measure of statistical distance (variability) between an individual score and center of
distribution
Score = mean + deviation
Score - mean = deviation
Deviation = individual score - mean
o Mean is the center of town (center of distribution)
o Subtract score from mean, we are finding how far from the center they are
o Deviation is like the distance on the map
Deviations always sum to zero
When computed relative to the mean
To compute the average deviation (distance)
o Ignore the negatives and add them all up, divide by the number of scores.
Standard Deviation
The average difference (distance) between a set of scores and the mean
Uses squaring to eliminate negative errors but is conceptually similar to averaging
absolute deviations
o Standard = typical
For population - greek symbol sigma
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Document Summary

Ex) using the center of town to create data in numbers: average distances, find the mean. Deviations always sum to zero: when computed relative to the mean, to compute the average deviation (distance) Ignore the negatives and add them all up, divide by the number of scores. To compute: deviation score is another name for error, (x-mu, score for one participant - population mean, distance between a score and the mean (center of distribution, square deviation scores, (x-mu)^2, deviation scores must sum to zero. The variance is the average squared deviation: to unsquare - you just square root. Ex) typical respondent is +/- 6. 87 points from the mean: average of the distances from the mean. A problem with the population formula: computes error relative to the true population mean, we will never know the true population mean, sample mean is not the same as the actual population mean.

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