Questions Discussion Board: Instructions

Purpose: The purpose of this assignment is to familiarize you the complexity of quantitative statistical analysis.

Instructions: For this discussion, please complete the following:

1. Review the assigned chapters in Polit and Beck (2017): i.e., chapters 18-20.
2. Identify five different facts or pieces of information that pertain to the topic.
3. Construct 3 multiple choice questions based on the information in the text and post these questions to the discussion

SOLUTION 

Five Facts from Polit & Beck (2017): Chapters 18–20

1. Statistical Significance vs. Clinical Significance: A statistically significant result does not necessarily mean the findings are clinically important. Statistical significance is often determined by a p-value (<0.05), while clinical significance refers to the practical importance of the effect in real-world settings.

2. Type I and Type II Errors: A Type I error occurs when the null hypothesis is incorrectly rejected (false positive), while a Type II error occurs when the null hypothesis is incorrectly accepted (false negative). The probability of a Type I error is denoted by alpha (α), commonly set at 0.05.

3. Confidence Intervals (CI): Confidence intervals provide a range within which the true population parameter is expected to lie. A 95% CI implies that if the study were repeated 100 times, the parameter would fall within this range in 95 of those repetitions.

4. t-tests and ANOVA: A t-test compares the means between two groups, while ANOVA (Analysis of Variance) compares means among three or more groups to detect significant differences.

5. Effect Size: Effect size quantifies the magnitude of the difference between groups, providing a sense of how meaningful the differences are, regardless of sample size. Common measures include Cohen’s d, eta squared (η²), and odds ratios.

Three Multiple Choice Questions

1. Which of the following best describes a Type II error in hypothesis testing?
A. Rejecting a true null hypothesis
B. Accepting a true null hypothesis
C. Rejecting a false null hypothesis
D. Accepting a false null hypothesis
Correct Answer: D

2. What does a 95% confidence interval indicate?
A. The result will occur 95% of the time
B. The population mean is guaranteed to lie within the interval
C. 95 out of 100 such intervals would contain the population parameter
D. The p-value is 0.95
Correct Answer: C

3. What is the primary difference between statistical significance and clinical significance?
A. Statistical significance is subjective; clinical is objective
B. Statistical significance uses qualitative data; clinical uses quantitative
C. Statistical significance is based on sample size; clinical significance is not
D. Statistical significance relates to probability; clinical significance relates to practical importance
Correct Answer: D

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