the numerical average. Obtained by summing all of the measurements in the distribution and dividing by the number of measurements in the distribution
Population Mean µ
(∑X)/N
Sample Mean M
(∑X)/n
Population Parameter
a quantity computed from the scores in a population.
Sample Statistic
a quantity computed from the scores in a sample.
Unbiased Estimator
a quantity which when all possible random samples of the same size are collected from a population and a mean is computed from each of the samples, then the mean of means equals the population parameter being estimated.
Median
the 50th percentile, the score value that has below it half of the measurements in the distribution and half the measurements above it.
Mode
the score value (or class interval) with the greatest frequency.
Variability
the extent to which the measurements in a distribution differ from one another.
Range
the largest score minus the smallest score.
Population Variance
the average of the squared deviations of each score from the population mean (µ). The symbol for the population variance is the Greek lowercase letter sigma to the power of two,σ².
Sum of Squares (SS)
the sum of the squared deviations of each score.
SS Sum of Squares
Σ(X-μ)² or ΣX² - ((ΣX)²)/N
σ² Population Variance
(∑(X- μ)²)/N or (∑X²-Nµ²)/N
Sample Variance
the sum of squared deviations of each score from M divided by n – 1. The symbol of the sample variance is s².
s² Sample Variance
SS/(n-1) or (∑X²-nM²)/(n-1)
Standard Score (z score)
a score that has been standardized by subtracting µ and dividing the difference by σ. This score indicates the number of standard deviations the observation is above or below the mean of the distribution.
z score formula
(X- μ)/σ
Raw Score from a zscore formula
X= μ+zσ
Normal Distribution
A unimodal and symmetrical distribution with both tails extending to infinity.
Standard Normal Distribution
a normal distribution with µ = 0 and σ = 0.
Author
darwinguevarra
ID
64351
Card Set
PSY571_Ch3-4_Glossary
Description
Ch. 2: Central Tendency and Variability
Ch. 4: z Scores and Normal Distributions