# 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.

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