Chapter 16 Marketing Research

statistical significance
a difference that is large enough that it is not likely to have occurred because of chance or sampling error
hypothesis
assumption or theory that a researcher or manager makes about some characteristic of the population under study
null hypothesis
the hypothesis of status quo, no difference, no effect
decision rule
rule or standard used to determine whether to reject or fail to reject the null hypothesis
type I error (α error)
rejection of the null hypothesis when, in fact, it is true
type II error (β error)
failure to reject the null hypothesis when, in fact it is false
independent samples
samples in which measurement of a variable in one population has no effect on measurement of the variable in the other
related samples
samples in which measurement of a variable in one population may influence measurement of the variable in the other
degrees of freedom
number of observations in a statistical problem that are free to vary
chi-square test
test of the goodness of fit between the observed distribution and the expected distribution of a variable
Z test
hypothesis test used for a single means if the sample is large enough and drawn at random
t test
hypothesis test used for a single means if the sample size is too small to use the Z test
hypothesis test of proportions
test to determine whether the difference between proportions is greater than would be expected because of sampling error
analysis of variance (ANOVA)
test for the differences among the means of two or more independent samples
F test
test of probability that a particular calculated value could have been due to chance
p value
exact probability of getting a computed test statistic that is due to chance. The smaller the p value, the smaller the probability that the observed result occurred by chance.