Data Analysis: T-Test and ANOVA (Statistics Project Sample)

Data Analysis: t-Test and ANOVA
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Data Analysis: Hypothesis Testing
This project used the Sun Coast Remediation dataset to perform t-test, and ANOVA hypothesis testing to assess the differences between variables or groups. The data analysis was conducted using the data analysis Toolpak in Excel.
Independent Samples t-Test: Hypothesis Testing
This parametric test is used to test the mean differences between two independent groups (Arkkelin, 2014). This project carried out an independent t-test to assess whether there is a significant difference between the safety training programs of Group A employees who were trained using the current program (the prior training, IV1) and Group B employees who were trained using a new program (revised training, IV2).
The hypotheses are as follows:
Ho4: There is no statistically significant difference in mean values of the DV between Group A (IV1), Prior Training, and group B (IV2), Revised Training.
Ha4: There is a statistically significant difference in mean values of the DV between Group A (IV1), Prior Training, and group B (IV2), Revised Training.
The table below shows the results from Excel
t-Test: Two-Sample Assuming Unequal Variances


 

Prior Training

Revised Training

Mean

69.7903

84.7742

Variance

122.00450

26.9646

Observations

62

62

Hypothesized Mean Difference

0


df

87


t Stat

-9.6666


P(T<=t) one-tail

9.699E-16


t Critical one-tail

1.6626


P(T<=t) two-tail

1.94E-15


t Critical two-tail

1.9876

 

Table SEQ Table \* ARABIC 1: Independent t-Test
The results indicate that the average values are lower for Group A employees who were trained using the current program (the prior training). Additionally, the results also show a p-value of 1.94 x 10-15 (two-tailed), which is less than 0.05 alpha level. Therefore, we reject the null hypothesis and accept the alternative hypothesis that there is a statistically significant difference in mean values of the DV between Group A (IV1), Prior Training, and group B (IV2), Revised Training. The company should consider replacing the prior safety training program with the revised program since the mean scores from the training show improvement.
Dependent Samples (Paired Samples) t-Test: Hypothesis Testing
A Dependent sample t-test is used to compare the means of two measurements drawn from the same object or individual (LibGuides, 2021). In a dependent t-test, a dependent variable is measured for one group both before and after exposure to an independent variable to determine if statistically significant differences exist. This project carried out a dependent sample t-test to assess whether the blood lead levels had increased while employees conducted lead remediation at the job sites. The data used was from forty-nine employees. The null and alternative hypotheses formulated are as follows:
Ho5: There is no statistically significant difference in the lead levels in the blood, pre-exposure and post-exposure.
Ha5: There is a statistically significant difference in the lead levels in the blood, pre-exposure and post-exposure.
The results from Excel are shown in the table below



 

Pre-Exposure μg/dL

Post-Exposure μg/dL

Mean

32.8571

33.2857

Variance

150.4583

155.5

Observations

49

49

Pearson Correlation

0.9922


Hypothesized Mean Difference

0


Df

48


t Stat

-1.9298


P(T<=t) one-tail

0.02978


t Critical one-tail

1.6772


P(T<=t) two-tail

0.05955


t Critical two-tail

2.01064

 

Table SEQ Table \* ARABIC 2: Paired Sample t-Test
The results in table 2 above indicate a p-value of 0.0596 (two-tailed), which is higher than an alpha level of 0.05. Therefore, we reject the alternative hypothesis and accept the null hypothesis that there is no statistically significant difference in the lead levels in the blood, pre-exposure and post-exposure, of employees working at job sites where lead remediation is being conducted.
ANOVA: Hypothesis Testing
ANOVA test of hypothesis is used to compare more than two means. It is used when comparing more than two groups or more than two levels of the independent variable (Sullivan, 2016). This project will carry out ANOVA to test if there is a significant difference between the four IVs (air, soil, water, and