Week 4 DQ 2 – Response to classmate Martha Grice, statistics homework help
Week 4 DQ 2 – Response to classmate Martha Grice, statistics homework help
Hello Dr. Ling and class,
H0: 1 – 2 = 0
The null hypothesis states that there is no difference between population means in regard to test scores.
H1: = 1 – 2 ≠ 0
The alternate hypothesis states that there is a difference between population means in regard to test scores.
To use the independent-samples t-test for data analysis, there are six assumptions that must be satisfied. There must be a single dependent variable that is measured on the continuous level, which is the exam score in this study. One independent variable that consists of two categorical, independent groups, which is composed of the group that was not exposed background noise during the math lesson and the group exposed to background noise during the math lesson. There is independence of observations, or no relationship between the observations in each group of the independent variable. To further satisfy the assumptions, there should be outliers in the data and results should be normally distributed for the dependent variable (exam scores) and for each independent variable group. Lastly, there should be homogeneity of variances (Independent-Samples, 2015; Gravetter & Wallnau, 2013).
To determine if the data satisfies the assumptions, Mary should start with determining the t-distribution by calculating the degrees of freedom (df). The larger the df, the closer to a normal distribution the data has (Gravetter & Wallnau, 2013). A boxplot offers a visual inspection for outliers in the data. Another test for determining whether data is normally distributed is the Shapiro-Wilk test. When the assumption of normality has been violated, the value will be less than .05. A value of greater than .05 means that the assumption of normality has not been violated (Independent-Samples, 2015). A pooled variance is calculated for normally distributed data by combining the data from the two categories in the independent variable, which then enables calculation of the estimated standard error and the t statistic (Gravetter & Wallnau, 2013). If the t statistic is in the critical region, the null hypothesis can be rejected. The probability, or p-value, will indicate significance of the results and whether the null hypothesis can be rejected. A p-value of 0.05 indicates that an observation is in the tail of the null distribution, which suggests that the null hypothesis can be rejected (Christie, Cliffe, Dawid, & Senn, 2011). A Cohen’s d is calculated to determine the strength of the size of the effect. A small effect is realized when the result is 0 to .20, a medium effect is seen when the effect is .20 to .50, and a large effect is noted when the effect is greater than .50 (Cohen, 1992).
Please respond to the above question using 250 words. Please also use at least 1 reference that is from a peer reviewed article or journal not a website reference. Please also cite the reference in APA 6th edition format.
