Statistical Concepts and Hypothesis Testing, Exams of Nursing

A wide range of statistical concepts and hypothesis testing methods, including the mann-whitney u test, t-test, variance, linear regression assumptions, scatter plots, chi-square test, survival analysis, external validity, normal distribution, p-values, random sampling, ordinal data, types of bias, nonparametric tests, systematic review steps, measures of spread, confidence intervals, anova, chi-square, type i and ii errors, imputation, central limit theorem, stratified sampling, measures of central tendency, hypothesis testing power, cohort studies, t-test assumptions, reliability, confounding variables, nonparametric methods, logistic regression, and systematic reviews. Explanations, rationales, and examples related to these statistical topics, making it a comprehensive resource for understanding the fundamentals of statistical analysis and hypothesis testing.

Typology: Exams

2023/2024

Available from 08/28/2024

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©GCU 2024/2025
HLT-362V
Applied Statistics
FINAL EXAM REVIEW
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HLT-362V

Applied Statistics

FINAL EXAM REVIEW

  1. Question: What statistical test is most appropriate for comparing the means of two independent groups, such as the average blood pressure of patients receiving two different treatments?
    • A) Chi-Square Test
    • B) T-test
    • C) ANOVA
    • D) Mann-Whitney U Test Answer : B) T-test Rationale: A T-test is specifically designed to compare the means of two independent samples.
  2. Question: In a clinical trial evaluating a new medication, the p-value obtained for the primary outcome is 0.03. What does this suggest about the null hypothesis?
    • A) It is likely true
    • B) It is rejected at a significance level of 0.
    • C) It should not be considered
    • D) It indicates a strong effect size Answer : B) It is rejected at a significance level of 0.

Rationale: The confidence interval provides a range for the expected or true difference based on the sample data.

  1. Question: Which of the following is an assumption of linear regression analysis?
    • A) Data must be normally distributed
    • B) The relationship between the predictors and outcomes must be linear
    • C) Homoscedasticity of residuals
    • D) All of the above Answer : D) All of the above Rationale: Linear regression relies on these key assumptions to function correctly.

Fill-in-the-Blank Questions

  1. Question: The ________ is a graphical representation that shows the relationship between two quantitative variables and helps identify correlations. Answer : Scatter plot Rationale: Scatter plots visualize relationships and correlations between two variables.
  1. Question: The ________ test is used to assess the strength of association between categorical variables. Answer : Chi-square Rationale: The chi-square test is designed for examining the association between categorical variables.
  2. Question: In survival analysis, the ________ function describes the probability of an event occurring by a certain time. Answer : Survival Rationale: The survival function reflects the time duration until an event occurs.
  3. Question: The extent to which the results of a study can be generalized to, or have relevance for settings beyond the study context is known as ________. Answer : External validity Rationale: External validity refers to the applicability of study findings in other contexts or populations.
  4. Question: In a normal distribution, approximately ________ percent of the data falls within one standard deviation of the mean. Answer : 68
  1. Question: In ordinal data, the intervals between data points are not equal. Answer : True Rationale: Ordinal data reflect a ranking order but don't denote equal distances between ranks.
  2. Question: The Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the original population's distribution. Answer : True Rationale: The Central Limit Theorem supports this concept, allowing for normal approximations under certain conditions.

Multiple Response Questions

  1. Question: Which of the following are types of bias that can affect statistical studies? (Select all that apply)
    • A) Selection Bias
    • B) Confirmation Bias
    • C) Attrition Bias
    • D) Performance Bias

Answer : A) Selection Bias, C) Attrition Bias, D) Performance Bias Rationale: These biases can distort study results. Confirmation bias is more about interpretation than statistical design.

  1. Question: Which of the following are examples of non- parametric tests? (Select all that apply)
    • A) Wilcoxon Signed-Rank Test
    • B) Kruskal-Wallis Test
    • C) ANOVA
    • D) Spearman's Rank Correlation Answer : A) Wilcoxon Signed-Rank Test, B) Kruskal-Wallis Test, D) Spearman's Rank Correlation Rationale: Non-parametric tests do not assume a normal distribution, unlike ANOVA.
  2. Question: When conducting a systematic review, which of the following steps should be included? (Select all that apply)
    • A) Formulating a clear research question
    • B) Selecting a representative sample
    • C) Conducting a comprehensive literature search
    • D) Analyzing data using meta-analysis methods

D) To calculate averages Correct Answer : A) To estimate population parameters Rationale: Confidence intervals provide a range of values that are likely to contain the population parameter, hence estimating it. Multiple Choice: Which statistical test is most appropriate for comparing the means of three or more independent groups? A) t-test B) ANOVA C) Chi-square test D) Mann-Whitney U test Correct Answer : B) ANOVA Rationale: ANOVA (Analysis of Variance) is specifically designed for comparing the means across multiple groups. Fill-in-the-Blank: The __ test is used to assess the relationship between two categorical variables. Correct Answer : Chi-square Rationale: The Chi-square test evaluates whether there is a significant association between two categorical variables.

True/False: A p-value of 0.05 indicates that there is a 5% probability that the null hypothesis is true. Correct Answer : False Rationale: A p-value of 0.05 suggests that there is a 5% probability of observing the data given that the null hypothesis is true, not the probability that the null hypothesis itself is true. Multiple Response: Which of the following are assumptions of linear regression? (Select all that apply) A) Linearity B) Homoscedasticity C) Independence of errors D) Normality of residuals Correct Answer s: A, B, C, D Rationale: All listed options are fundamental assumptions necessary for the validity of linear regression analysis. Multiple Choice: In a clinical trial, a Type I error occurs when: A) A true null hypothesis is incorrectly rejected B) A false null hypothesis is incorrectly accepted C) The sample size is too small D) The test lacks power

B) It reduces sampling bias C) It guarantees equal representation D) It is simpler to analyze Correct Answer : B) It reduces sampling bias Rationale: Stratified sampling ensures that specific subgroups are adequately represented, thereby minimizing bias in the sample. Multiple Response: Which of the following metrics are considered measures of central tendency? (Select all that apply) A) Mean B) Median C) Mode D) Range Correct Answer s: A, B, C Rationale: Mean, median, and mode are all measures that summarize a set of data by identifying the central point within that dataset. Multiple Choice: In hypothesis testing, the power of a test is defined as: A) The probability of rejecting the null hypothesis when it is false

B) The probability of accepting the null hypothesis when it is true C) The probability of making a Type I error D) The probability of making a Type II error Correct Answer : A) The probability of rejecting the null hypothesis when it is false Rationale: Power reflects the test's ability to detect an effect when there is one. Fill-in-the-Blank: The __ coefficient measures the strength and direction of the linear relationship between two variables. Correct Answer : Correlation Rationale: The correlation coefficient quantifies the degree to which two variables are related. True/False: A cohort study is a type of observational study where participants are followed over time to assess outcomes. Correct Answer : True Rationale: Cohort studies involve tracking a group over time to observe the development of particular outcomes or diseases. Multiple Choice: Which of the following is a requirement for using a t-test?

C) A variable that is related to both the independent and dependent variables D) A variable that has no effect on the outcome Correct Answer : C) A variable that is related to both the independent and dependent variables Rationale: A confounding variable can obscure the true relationship between the independent and dependent variables. Fill-in-the-Blank: The __ method is often used for analyzing data that does not meet the assumptions of parametric tests. Correct Answer : Non-parametric Rationale: Non-parametric tests are suitable for data that does not conform to normal distribution or when sample sizes are small. True/False: In logistic regression, the dependent variable must be continuous. Correct Answer : False Rationale: Logistic regression is used when the dependent variable is categorical, typically binary. Multiple Choice: Which of the following best describes a systematic review? A) A summary of individual studies without analysis

B) A comprehensive overview of all available data on a topic C) A method of collecting qualitative data D) A type of statistical analysis Correct Answer : B) A comprehensive overview of all available data on a topic Rationale: A systematic review synthesizes all relevant studies on a specific research question, providing a high level of evidence. Multiple Response: Which of the following are types of data measurement scales? (Select all that apply) A) Nominal B) Ordinal C) Interval D) Ratio Correct Answer s: A, B, C, D Rationale: All four options represent different scales for measuring data, each with unique properties and applications in Multiple Choice: Which statistical test is most appropriate for comparing the means of two independent groups with normally distributed data? a) Chi-square test

b) Homoscedasticity c) Normal distribution of residuals d) Independence of observations Correct Answer s: a) Linearity, b) Homoscedasticity, c) Normal distribution of residuals, d) Independence of observations Rationale: These are the key assumptions that underpin the validity of linear regression analysis.