Asked by Stephanie Schiwietz on Jun 25, 2024
Verified
An online retailer wanted to see if purchasing patterns were different between male consumers and female consumers.Males were found to have spent a mean of $85 per month on online purchases,whereas females were found to have spent a mean of $78 per month.An independent samples t-test was conducted to assess the observed difference between the two groups of consumers.A two-tailed test was applied.The value of the t-Test Statistic for the observed differences was -4.7,where the p-value is less than 0.0002.What conclusion can be made about the observed difference?
A) There is a statistically significant difference between the two groups of consumers.
B) There is statistically no significant difference between the two groups of consumers.
C) The independent samples t-test is not the appropriate hypothesis-testing procedure.
D) There is not sufficient information to conclude.
Independent Samples T-Test
A statistical test used to determine if there are significant differences between the means of two unrelated groups on a continuous outcome.
Statistically Significant Difference
A difference between two groups or variables that is unlikely to have occurred due to chance alone, as determined by statistical analysis.
Consumers
Individuals who purchase goods and services for personal use.
- Gain proficiency in computing and interpreting test statistics.
- Gain insight into the meaning and effects of the p-value when conducting hypothesis tests.
- Comprehend the situations in which the null hypothesis is more apt to be rejected or not rejected.
Verified Answer
TC
Taryn CurlessJul 02, 2024
Final Answer :
A
Explanation :
The t-Test Statistic value of -4.7 indicates that the observed difference between the mean spending of male and female consumers is large and unlikely to occur by chance. The p-value of less than 0.0002 also supports this conclusion. Therefore, there is a statistically significant difference between the two groups of consumers.
Learning Objectives
- Gain proficiency in computing and interpreting test statistics.
- Gain insight into the meaning and effects of the p-value when conducting hypothesis tests.
- Comprehend the situations in which the null hypothesis is more apt to be rejected or not rejected.