What statistical test would BEST determine if there is a significant difference in brand perception between two consumer groups?

An independent samples t-test is the statistical test that would BEST determine if there is a significant difference in brand perception between two consumer groups, assuming certain conditions are met. An independent samples t-test is used to compare the means of two independent groups to determine if there is a statistically significant difference between them. In this context, 'brand perception' would typically be measured using a rating scale or a composite score based on multiple questions about brand attributes (e.g., quality, style, value). The two 'consumer groups' could be defined by demographics (e.g., age, gender), psychographics (e.g., lifestyle, values), or exposure to different marketing campaigns. The t-test calculates a t-statistic, which is a measure of the difference between the group means relative to the variability within the groups. A p-value is then calculated, which represents the probability of observing the obtained results (or more extreme results) if there were no actual difference between the groups. If the p-value is below a predetermined significance level (e.g., 0.05), the null hypothesis (that there is no difference in brand perception) is rejected, and it is concluded that there is a statistically significant difference between the groups. For example, a fashion brand might use a t-test to compare the perception of its brand among consumers who have seen its new ad campaign versus those who have not. It is important to ensure that the data meets the assumptions of the t-test, such as normality and homogeneity of variance. If these assumptions are not met, non-parametric alternatives like the Mann-Whitney U test could be used. Chi-square tests are used for categorical data, and ANOVA is used for comparing more than two groups, making the independent samples t-test the most appropriate choice for this scenario.