HSCI 190 Module 04 Homework:
Statistical Tests & Analysis Concepts
Practice Questions And Answers
, Module 04 Homework Problems
Part 1 – Consolidating Concepts (5 marks)
1. Describe the situations where you would use a one sample t test versus a one sample z
test? (1 mark)
You would use a one sample t test when the sample size is small (n<30) and the populations
standard deviation is unknown, whereas a one sample z test would be used when the sample
size is large (n>30), and the population standard deviation is known.
2. Describe the main difference between dependent and independent t tests? Provide your
own original example of when you would use a dependent t test vs. independent t test.
Note: do not copy the examples from the modules. (2 marks)
The main difference between dependent and independent t test is the relationship between
groups. Dependent t test is used when comparing paired data, where the score on one measure
affects the score on another, while independent t test is used when comparing unpaired data,
where scores are not dependent on each other. An example of a dependent t test would be a
scientist measuring cholesterol levels on a person before a researched diet and after one month
of the same diet. An example of an independent t test would be comparing test scores between
a group of students who received tutoring (Group A) and a group of students who didn’t receive
any additional help (Group B).
3. Describe how parametric tests differ from nonparametric tests? (1 mark)
Parametric tests differ from nonparametric tests as parametric tests require more assumption, a
scale data & normal distribution. There are more assumptions to be met (more powerful), and
highly sensitive to outliers and measurement error. Parametric test, such as the independent t
tests and dependent t tests, are statistical methods for comparing the means of two groups.
They assume that the data follows a specific distribution and meets certain conditions, like
equal variances for independent groups or normality of differences for dependent.
Nonparametric tests require less assumptions, accepting either scale or ordinal data & does not
require normality. Independent t test score is used when each score is independent and
dependent t test is used when scores are dependent upon each other. There’s also less
assumptions in nonparametric tests, meaning it is less sensitive to outliers and measurement
error compared to parametric tests. Nonparametric tests like the Mann U Whitney test and the
Wilcoxon signed-ran test, are employed when parametric tests assumptions damaged, for
instance when the data is skewed, ordinal, or when sample sizes are small. Mann U Whittney &
) 1
Statistical Tests & Analysis Concepts
Practice Questions And Answers
, Module 04 Homework Problems
Part 1 – Consolidating Concepts (5 marks)
1. Describe the situations where you would use a one sample t test versus a one sample z
test? (1 mark)
You would use a one sample t test when the sample size is small (n<30) and the populations
standard deviation is unknown, whereas a one sample z test would be used when the sample
size is large (n>30), and the population standard deviation is known.
2. Describe the main difference between dependent and independent t tests? Provide your
own original example of when you would use a dependent t test vs. independent t test.
Note: do not copy the examples from the modules. (2 marks)
The main difference between dependent and independent t test is the relationship between
groups. Dependent t test is used when comparing paired data, where the score on one measure
affects the score on another, while independent t test is used when comparing unpaired data,
where scores are not dependent on each other. An example of a dependent t test would be a
scientist measuring cholesterol levels on a person before a researched diet and after one month
of the same diet. An example of an independent t test would be comparing test scores between
a group of students who received tutoring (Group A) and a group of students who didn’t receive
any additional help (Group B).
3. Describe how parametric tests differ from nonparametric tests? (1 mark)
Parametric tests differ from nonparametric tests as parametric tests require more assumption, a
scale data & normal distribution. There are more assumptions to be met (more powerful), and
highly sensitive to outliers and measurement error. Parametric test, such as the independent t
tests and dependent t tests, are statistical methods for comparing the means of two groups.
They assume that the data follows a specific distribution and meets certain conditions, like
equal variances for independent groups or normality of differences for dependent.
Nonparametric tests require less assumptions, accepting either scale or ordinal data & does not
require normality. Independent t test score is used when each score is independent and
dependent t test is used when scores are dependent upon each other. There’s also less
assumptions in nonparametric tests, meaning it is less sensitive to outliers and measurement
error compared to parametric tests. Nonparametric tests like the Mann U Whitney test and the
Wilcoxon signed-ran test, are employed when parametric tests assumptions damaged, for
instance when the data is skewed, ordinal, or when sample sizes are small. Mann U Whittney &
) 1
