University Assignment: Hypothesis Testing with Sun Coast Data

This assignment presents a comprehensive analysis of the Sun Coast Remediation data set, focusing on hypothesis testing using statistical methods. The solution includes the application of an independent samples t-test to compare the average training scores of two groups (Group A and Group B), a dependent samples t-test (paired t-test) to examine the difference in blood concentration levels before and after exposure, and a one-way ANOVA to assess the differences in return on investments across four different categories (Air, Soil, Water, and Training). Each test includes the formulation of null and alternative hypotheses, the presentation of Excel output tables, and a detailed interpretation of the results, including the p-value, alpha level, and conclusions regarding the acceptance or rejection of the null hypothesis. The assignment also references relevant research papers to support the statistical methods and interpretations. The results indicate significant differences in training scores between groups, no significant difference in blood concentration levels, and significant differences in return on investments across the categories.

Running Head: HYPOTHESIS TESTING WITH SUN COAST REMEDIATION DATA SET
Title: Hypothesis Testing with Coast Remediation Data Set
Student Name:
University Name:

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Independent Samples t-Test:
Null hypothesis: H01: There is no statistically significant difference between average
“Group-A Prior Training Scores” and “Group-B Revised Training Scores”.
Alternate hypothesis: HA1: There is statistically significant difference between
average “Group-A Prior Training Scores” and “Group-B Revised Training Scores”.
Table 1: Excel Output of Independent t-test with Unequal Variances
Results:
Average training score of Group-B (M = 84.77, SD = 5.19) was higher than average
score of training score of Group-A (M = 69.79, SD = 11.05). The results of the two-tailed
independent t-test indicates that the p-value < 0.01, implying average revised training score
of Group-B is significantly different (higher) than prior training scores of Group-A at α= 1%
level of significance. Hence, the null hypothesis assuming the equality between the average
scores of the two groups is rejected against the alternate hypothesis at α = 1% level of
significance. Therefore, it can be concluded that average revised training scores for Group-B
is significantly higher than average prior scores of Group-A (Lakens, 2017).

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Dependent Samples t-Test:
Null hypothesis: H01: There is no statistically significant difference between average
concentration in blood of the employees for “Pre-Exposure” and “Post-Exposure”.
Alternate hypothesis: HA1: There is statistically significant difference between
average concentration in blood of the employees for “Pre-Exposure” and “Post-Exposure”.
Table 2: Excel Output of Dependent t-test (Paired t-test)
Results:
Average concentration in blood in Post-Exposure (M = 33.29, SD = 12.47) is higher
than average concentration in blood in Pre-Exposure (M = 32.86, SD = 12.27). The results of
the two-tailed dependent t-test indicates that the p-value = 0.06 > 0.05, implying that there is
no statistically significant difference between concentration in blood between Post-Exposure
and Pre-Exposure groups at α= 5% level of significance. Hence, the null hypothesis
assuming the equality between the average scores of Pre-Exposure and Post-Exposure failed
to get rejected (accepted) against the alternate hypothesis at α= 5% level of significance.
Therefore, it can be concluded that average concentration for both the exposure groups are
statistically equal (Maggio, & Sawilowsky, 2014).

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One-way ANOVA:
Null hypothesis: H01: There is no statistically significant difference between average
return on investments among Air, Soil, Water, and Training.
Alternate hypothesis: HA1: There is at least one statistically significant different
average return on investments among Air, Soil, Water, and Training.
Table 3: Excel Output of One-Way ANOVA
Results:
Average return on investment in Soil (M = 9.1, SD = 1.74) is higher than other three
groups. The results of the One-Way independent ANOVA indicates that the p-value < 0.01,
implying that there is at least one group for which return on investment is significantly
different among all the groups at α = 1% level of significance (Moder, 2010). Hence, the null
hypothesis assuming the equality between the average return on investments among Air, Soil,
Water, and Training is rejected at α= 5% level of significance. Therefore, it can be
concluded that average return on investment in Soil is significantly different from other three
groups. A post-hoc analysis is required to find pairwise differences between the groups
(Kucuk, Eyuboglu, Kucuk, & Degirmencioglu, 2016).

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References
Kucuk, U., Eyuboglu, M., Kucuk, H. O., & Degirmencioglu, G. (2016). Importance of using
proper post hoc test with ANOVA. International journal of cardiology, 209, 346.
Lakens, D. (2017). Equivalence tests: a practical primer for t tests, correlations, and meta-
analyses. Social psychological and personality science, 8(4), 355-362.
Maggio, S., & Sawilowsky, S. (2014). JMASM 33: A Two Dependent Samples Maximum
Test Calculator: Excel. Journal of Modern Applied Statistical Methods, 13(1), 32.
Moder, K. (2010). Alternatives to F-test in one way ANOVA in case of heterogeneity of
variances (a simulation study). Psychological Test and Assessment Modeling, 52(4),
343-353.