Psych 241 Chapter 4 textbook notes not finished

population standard deviation

(all greek o) o= square root of the quantity o^2= square root of the quantity SS/N

population variance

(greek o) o^2 = SS/N

quasi experiment

2 variables are measured and there is only 1 group of participants

FYI if the scores in a distribution are all the same, then there is no variability. If there are small differences between scores, then the variability is small, and if there are large differences between scores, then the variability is large

FYI variability describes the distribution and tells whether the scores are clustered close together or are spread out over a large distance. Variability measures how well an individual score represents the entire distribution. Variability provides information about how much error to expect if you are using a sample to represent a population

sum of the squared deviation scores

SS=

Three General Principles of Science 1. Prediction: We only accept explanations that have been shown to successfully predict behavior.

Three General Principles of Science 2. Replication: We must be able to replicate (confirm) the findings—another person should be able to do the exact same research.

upper real limit (URL) for the largest score. and lower real limit (LRL) for the smallest score ex: if the range is from

When the scores are measurements of a continuous variable, the range can be defined as the difference between

deviation

X-u(greekU)

standard deviation

_____ uses the mean of the distribution as a reference point and measures variability by considering the distance between each score and the mean

below the mean by a distance of 6 points

a deviation score of -6 corresponds to a score that is

1 variable for each participant and compares 2 groups of scores, control group and experimental group

a typical experimental research study measures

SS= EX^2 - (EX)^2/N

computation formula for SS

variable

condition that can change or take on different values

organize and summarize data

descriptive statistics..

X-u (greek U)

deviation

population variance

equals the mean squared deviation. Variance i the average squared distance from the mean

popular variance

equals the mean squared deviation. Variance is the average squared distance from the mean

unbiased

if the average value of the statistic is equal to the population parameter (the average value of the statistic is obtained from all the possible samples for a specific sample size, n)

standard deviation

provides a measure of the standard, or average distance from the mean and describes whether the scores are clustered closely around the mean or are widely scattered

variability

provides a quantitative measure of the differences between scores in a distribution and describes the degree to which the scores are spread out or clustered together

variance = SS/N

remember that variance is defined as the mean squared deviation. The mean is the sum of the squared deviations divided by N, so the equation for the population variance is

less variable than their populations

samples consistently tend to be

standard deviation = square root of the quantity SS/N

standard deviation is the square root of variance, so the equation for the population standard deviation is

popular variance is the sum of squared deviation scores divided by the number of scores

standard deviation is the squared quantity value of Sum of squared deviation scores divided by the number of scores

to find the standard deviation: add the deviations and compute the average find the deviation (distance from the mean) for each score square each deviation find the average of squared deviations, this is the variance Take the square root of the variance. This is the standard deviation (standard distance from the mean)

standard deviation measures the standard distance from the mean, and variance measures the average squared distance from the mean

the square root of variance

standard deviation=

equal to zero

sum of deviation scores will always be equal to zero

sum of squared deviations (SS)

sum of squared deviation scores

biased

the average value of the statistic either underestimates or overestimates the corresponding population parameter

1)add the deviations and compute the average 2)find the deviation (distance from the mean) for each score 3)square each deviation 4)find the average of the squared deviations, this is the variance 5)take the square root of the variance. this is the standard deviation (the standard distance from the mean)

the calculation of variance and standard deviation

range, from the smallest score to the largest score

the distance covered by the scores in a distribution

use the limited information from samples to draw general conclusions about populations

the goal of inferential statistics is to

degrees of freedom

the sample variance are defined as n - 1. the degrees of freedom determine the number of scores in the sample that are independent and free to vary

standard deviation

the square root of the variance and provides a measure of the standard, or average, distance from the mean

o (greek o)

the symbol for the population standard deciation

o^2 (greek o squared)(standard deviation is the square root of the variance)

to emphasize the relationship between standard deviation and variance we use

in order to get the mean squared deviation, which is called population variance you have to square each deviation score before taking the mean then you take the square root of that to get the standard deviation score of variance

to find the standard deviation score take each scores individual deviation and then take the mean of each of those individual scores equation" X-u (greeku) should add up to 0

ordinal

using letter grades to classify students is ____ scale of measurement

SS = EX^2 - quantity (EX)^2/n

using sample notation the computational formula for SS

population

variability in a sample is smaller than that of a