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Week 3 Assignment
The independent samples T-test is a parametric test that is used to compare the
means of two independent samples as the name suggests (Fagerland, 2012). This is to
find out if the two population means are significantly different based on the samples
used. The samples used must be random and must meet the normality, independence,
and variance homogeneity assumptions (Wonnacott & Wonnacott, 1990).
The independent variable for this case will be a categorical or nominal variable;
gender while dependent variable will be a continuous variable; Height. The independent
samples T-test will, therefore, be used to test whether there is a mean difference in the
height of males and females, which are the two categories in the categorical variable
Is there a statistically significant gender height mean difference?
H0: There is no statistically significant gender height mean difference.
H1: There is a statistically significant gender height mean difference.
A two-tailed independent samples T-test was conducted at the 0.5 level of
significance to find out whether there is a statistically significant difference in the mean
height of males and females. The independent and dependent variables for the test were
gender and height consecutively. The test value (Levene’s test) for the assumption
assessment (p = 0.3, ˃.05) therefore not significant. The results were significant, t(103) =
0.067, p = .947. There is, therefore, a significant difference in the mean height of
individuals from the male (M = 169.982, SD = 17.5649) and female (M = 170.204, SD =
16.3171) genders at the 95% level of significance. Figure 1 is a box depicting height by
gender. The SPSS output is provided in the Appendix
Figure 1. Boxplot depicting height scores by gender
Fagerland, M. W. (2012). t-tests, non-parametric tests, and large studies—a paradox of
statistical practice?. BMC Medical Research Methodology, 12(1), 78
Wonnacott, T. H., & Wonnacott, R. J. (1990). Introductory statistics (Vol. 5). New York:
Appendix – Independent Samples t-Test SPSS Output
Independent Samples Test
Levene's Test for Equality of
t-test for Equality of Means
Equal variances assumed
Equal variances not
Week 4 Assignment
One-Way Analysis of Variance (ANOVA)
One way Analysis of Variance is used to evaluate for the statistical significance in
the difference of means of two or more unrelated independent groups. The one way
ANOVA statistic is termed an omnibus statistic given that it doesn’t specifically state the
groups that are statistically different from each other (Statistics, 2013). As a consequence,
post hoc tests are conducted to establish what specific independent groups are different
from the others in order to add more meaning to the generated ANOVA results
(Frankfort-Nachmias & Leon-Guerrero, 2015).
This research will seek to find out if a statistically significant difference exists in
the GPA scores of individuals by their ethnicity. As such, the one way ANOVA is the
most appropriate statistical test to be utilized. The independent variable (Ethnicity) is
measured at the nominal level while the dependent variable (GPA) is measured at the
Is there a statistically significant difference in the GPA scores among Hispanic,
African American, Caucasian, Native American, Asian American, and other individuals?
Null Hypothesis (H0): There is not a statistically significant difference in the GPA
scores among Hispanic, African American, Caucasian, Native American, Asian
American and other individuals.
Alternative Hypothesis (H1): There is a statistically significant difference in the
GPA scores among Hispanic, African American, Caucasian, Native American, Asian
American and other individuals.
In this subheading, I will present descriptive statistics, discuss testing of the
assumptions, present inferential statistic results, and conclude with a concise summary.
A total of 105 individuals participated in the study. The assumption of equality
variances (Levene’s test, p = .484) was evaluated with no violati...