Using and Interpreting Statistics Case Study 2022

Running head: USING AND INTERPRETING STATISTICS 1
Using and Interpreting Statistics
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USING AND INTERPRETING STATISTICS 2
Using and Interpreting Statistics
Question 1
Correlation does not equal Causation
Although correlation is a statistical method which measures the degree of a linear
relationship between a pair of variables, a measure of correlation does not include an
explanation behind the relationship (Singh, 2018). That is, correlation analysis focuses on the
existence or nonexistence of a relationship but does not explain why the relationship exists.
Therefore, just because two variables are related does not mean that one causes the other.
Data Characteristics Necessary to Calculate Person Correlation Coefficient
For the Pearson Correlation Coefficient to be calculated, the data must follow a
normal distribution (Pal, 2017). Besides, the data must meet the homoscedasticity condition.
This implies the size of the error term is the same for all values of independent variables in
the data. Besides, the data must follow a linear relationship, must be in paired observations,
and should not have outliers (Pal, 2017).
A study that Would Apply Pearson Correlation Coefficient as an Appropriate Statistic
Suppose a researcher wanted to determine if there is an association between cancer
and drinking hot tea. A Pearson correlation coefficient would be an appropriate statistic.
Question 2
Conditions in Which Nonparametric test is would be better than Parametric Test
Nonparametric test or distribution-free tests overcomes the need for the data to follow
or meet required assumptions such as those of a normal distribution (Corty, 2016).
Accordingly, several conditions exist that warrants selection of a nonparametric test over a