Marketing Research

Marketing Research Aaker, Kumar, Day Ninth Edition Instructor’s Presentation Slides

Chapter Seventeen Hypothesis Testing: Basic Concepts and Tests of Association

Assumption (hypothesis) made about a population parameter (not sample parameter) • Purpose of Hypothesis Testing • To make a judgment about the difference between two sample statistics or between sample statistic and a hypothesized population parameter • Evidence has to be evaluated statistically before arriving at a conclusion regarding the hypothesis. • Depends on whether information generated from the sample is with fewer or larger observations Hypothesis Testing: Basic Concepts

The null hypothesis (Ho) is tested against the alternative hypothesis (Ha). At least the null hypothesis is stated. Decide upon the criteria to be used in making the decision whether to “reject” or "not reject" the null hypothesis. Hypothesis Testing

Hypothesis Testing Process Problem Definition Clearly state the null and alternative hypotheses Determine the degrees of freedom Choose the relevant test and the appropriate probability distribution Decide if one-or two-tailed test Determine the significance level Choose the critical value Compare test statistic & critical value Compute relevant test statistic Does the test statistic fall in the critical region? Do not reject null Reject null

Three Criteria Used To Decide Critical Value (Whether To Accept or Reject Null Hypothesis): Significance Level Degrees of Freedom One or Two Tailed Test Basic Concepts of Hypothesis Testing

Indicates the percentage of sample means that is outside the cut-off limits (critical value) The higher the significance level () used for testing a hypothesis, the higher the probability of rejecting a null hypothesis when it is true (Type I error) Accepting a null hypothesis when it is falseis called a Type II error and its probability is () When choosing a level of significance, there is an inherent tradeoff between these two types of errors A good test of hypothesis should reject a null hypothesis when it is false Significance Level

Relationship between Type I & Type II Errors

Relationship between Type I & Type II Errors (Contd.)

Power of hypothesis test • (1 - ) should be as high as possible • Degrees of Freedom • The number or bits of "free" or unconstrained data used in calculating a sample statistic or test statistic • A sample mean (X) has `n' degree of freedom • A sample variance (s2) has (n-1) degrees of freedom Choosing The Critical Value

Hypothesis Testing & Associated Statistical Tests

One-tailed Hypothesis Test • Determines whether a particular population parameter is larger or smaller than some predefined value • Uses one critical value of test statistic • Two-tailed Hypothesis Test • Determines the likelihood that a population parameter is within certain upper and lower bounds • May use one or two critical values One or Two-tail Test

Select the appropriate probability distribution based on two criteria • Size of the sample • Whether the population standard deviation is known or not Basic Concepts of Hypothesis Testing (Contd.)

Hypothesis Testing

In Marketing Applications, Chi-square Statistic is used as: • Test of Independence • Are there associations between two or more variables in a study? • Test of Goodness of Fit • Is there a significant difference between an observed frequency distribution and a theoretical frequency distribution? • Statistical Independence • Two variables are statistically independent if a knowledge of one would offer no information as to the identity of the other Cross-tabulation and Chi Square

The Concept of Statistical Independence If n is equal to 200 and Eiis the number of outcomes expected in cell i,

Chi-Square As a Test of Independence

Null Hypothesis Ho Two (nominally scaled) variables are statistically independent Alternative Hypothesis Ha The two variables are not independent Use Chi-square distribution to test. Chi-Square As a Test of Independence (Contd.)

A probability distribution Total area under the curve is 1.0 A different chi-square distribution is associated with different degrees of freedom Chi-square Distribution Cutoff points of the chi-square distribution function

Degrees of Freedom Number of degrees of freedom, v = (r - 1) * (c - 1) r = number of rows in contingency table c = number of columns Mean of chi-squared distribution = Degree of freedom (v) Variance = 2v Chi-square Distribution (Contd.)

Measures of the difference between the actual numbers observed in cell i (Oi), and number expected (Ei) under assumption of statistical independence if the null hypothesis were true With (r-1)*(c-1) degrees of freedom Oi = observed number in cell i Ei = number in cell iexpected under independence r = number of rows c = number of columns • Expected frequency in each cell, Ei = pc * pr * n Where pc and pr are proportions for independent variables n is the total number of observations Chi-square Statistic (2)

Chi-square Step-by-Step

Measured by contingency coefficient 0 = no association (i.e., Variables are statistically independent) Maximum value depends on the size of table Compare only tables of same size Strength of Association

It is basically proportional to sample size • Difficult to interpret in absolute sense and compare cross-tabs of unequal size • It has no upper bound • Difficult to obtain a feel for its value • Does not indicate how two variables are related Limitations of Chi-square as an Association Measure

Measures based on Chi-Square Measures of Association for Nominal Variables Phi-squared Cramer’s V

Used to investigate how well the observed pattern fits the expected pattern Researcher may determine whether population distribution corresponds to either a normal, Poisson or binomial distribution Chi-square Goodness of Fit • To determine degrees of freedom: • Employ (k-1) rule • Subtract an additional degree of freedom for each population parameter that has to be estimated from the sample data

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