Reference no: EM13990363

T-test (1 of 2)

Difference testing is used primarily to identify if there is a detectable difference between products, services, people, or situations. These tests are often conducted in business situations to:

• Ensure a change in formulation or production introduces no significant change in the end product or service.
• Substantiate a claim of a new or improved product or service
• Confirm that a new ingredient/supplier does not affect the perceived attributes of the product or service.
• Track changes during shelf life of a product or the length of time of a service.

Differences Between Two Independent Sample Means:

Coke vs. Pepsi. Independent sample t-tests are used to compare the means of two independently sampled groups (ex., do those drinking Coke differ on a performance variable, or the numbers of cans consumed in one week) compared to those drinking Pepsi. The individuals are randomly assigned to the Coke and Pepsi groups. With a confidence interval of ≤.05 (corresponding probability level of 95%) the researcher concludes the two groups are significantly different in their means (average consumption rate of Coke and Pepsi over a one week period of time) if the t-test value meets or exceeds the required critical value. If the t value does not meet the critical t value required then the research investigator simply concludes that no differences exist. Further explanation is not required. Presented below is a more useable situation.

Any conclusion drawn for the t-test statistical process is only as good as the research question asked and the null hypothesis formulated. T-tests are only used for two sample groups, either on a pre post-test basis or between two samples (independent or dependent). The t-test is optimized to deal with small sample numbers which is often the case with managers in any business. When samples are excessively large the t test becomes difficult to manage due to the mathematical calculations involved.

Calculate the "t" value for independent groups for the following data using the formula presented in the module. Check the accuracy of your calculations. Using the raw measurement data presented above, determine whether or not there exists a statistically significant difference between the salaries of female and male human resource managers using the appropriate t-test. Develop a research question, testable hypothesis, confidence level, and degrees of freedom. Draw the appropriate conclusions with respect to female and male HR salary levels. Report the required "t" critical values based on the degrees of freedom. Your response should be 2-3 pages.

Salary Level
Female HR Directors	Male HR Directors
$50,000	$58,000
$75,000	$69,000
$72,000	$73,000
$67,000	$67,000
$54,000	$55,000
$58,000	$63,000
$52,000	$53,000
$68,000	$70,000
$71,000	$69,000
$55,000	$60,000
*Do not forget what we all learned in high school about "0"s