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CNSL 503 MODULE 3 PRACTICE EXAM|| QUESTIONS AND 100% CORRECT ANSWERS 2025/2026 ALREADY, Exams of Statistics

Geneva CollegeStatistics

CNSL 503 MODULE 3 PRACTICE EXAM|| QUESTIONS AND 100% CORRECT ANSWERS 2025/2026 ALREADY GRADED A+|| LATEST AND COMPLETE UPDATE WITH VERIFIED SOLUTIONS GUARANTEED PASS!!!

Typology: Exams

2024/2025

Available from 07/02/2025

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CNSL 503 MODULE 3 PRACTICE EXAM||
QUESTIONS AND 100% CORRECT ANSWERS
2025/2026 ALREADY GRADED A+|| LATEST AND
COMPLETE UPDATE WITH VERIFIED
SOLUTIONS GUARANTEED PASS!!!
What is the difference between a statistic and a parameter?
A statistic is a descriptive statistical result that is generated from a sample, whereas
a parameter is a statistical result from a population.
What is the difference between cluster sampling and stratified sampling?
Cluster sampling involves dividing a population into random groups called
clusters, whereas stratified sampling involves identifying strata within a population
and recruiting proportional numbers from random samples of each stratum.
What is the difference between sampling and nonsampling error?
Sampling error is any deviation between a statistic and a parameter that occurs due
to chance. Nonsampling errors usually result from problems with data collection.
Examples of sources of nonsampling error include measurement bias, response
bias, and selection bias.
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Download CNSL 503 MODULE 3 PRACTICE EXAM|| QUESTIONS AND 100% CORRECT ANSWERS 2025/2026 ALREADY and more Exams Statistics in PDF only on Docsity!

CNSL 503 MODULE 3 PRACTICE EXAM||

QUESTIONS AND 100% CORRECT ANSWERS

2025/2026 ALREADY GRADED A+|| LATEST AND

COMPLETE UPDATE WITH VERIFIED

SOLUTIONS GUARANTEED PASS!!!

What is the difference between a statistic and a parameter? A statistic is a descriptive statistical result that is generated from a sample, whereas a parameter is a statistical result from a population. What is the difference between cluster sampling and stratified sampling? Cluster sampling involves dividing a population into random groups called clusters, whereas stratified sampling involves identifying strata within a population and recruiting proportional numbers from random samples of each stratum. What is the difference between sampling and nonsampling error? Sampling error is any deviation between a statistic and a parameter that occurs due to chance. Nonsampling errors usually result from problems with data collection. Examples of sources of nonsampling error include measurement bias, response bias, and selection bias.

Why is it important that researchers practice ethical sampling techniques and data collection? To conduct data collection, analysis, and interpretation ethically is of the utmost importance because it is the responsibility of researchers to ensure that all information that they present to other researchers and to the public is accurate. Failing to implement measures to minimize bias and error can cause misleading results and false information to propagate. Distribution of sample means Refers to a sample statistic distribution that contains the frequencies of the means of all possible samples of a given size from a population Which of the following statements is TRUE regarding a distribution of sample means? The mean of the distribution of sample means is equal to the population mean What theory identifies several key characteristics of a distribution of sample means? Central limit theorem Sample size is considered large if it contains at least members. 30

What term is used to delineate a sample statistic that deviates from a population parameter to a degree that appears to be beyond chance? Statistically significant What is the general value that is used to determine statistical significance?

Which of the following terms describes a claim that is made about a particular population parameter? Hypothesis What is the difference between the null and alternative hypotheses? The null hypothesis (H0) is the beginning assumption of any hypothesis test that makes a particular claim about the value of a population parameter and, in essence, claims that an observed effect or difference does not exist. The alternative hypothesis (Ha) states that the value of the population parameter differs from the value claimed in the null hypothesis and, in essence, claims that an observed result or difference does exist. Identify the 4 steps in hypothesis testing

  1. State the null (H0) and alternative (Ha) hypotheses.
  1. Establish the important cutoff (critical) values by which to measure the observed result to determine statistical significance.
  2. Calculate the test statistic.
  3. Compare the test statistic to the critical values, and decide if the null hypothesis (H0) should be rejecting or not. What is the difference between Type I and Type II errors? Type I errors are false positives, whereas Type II errors are false negatives. What is the purpose of a significance level? The significance level (α) is the value that the P-value of a test statistic can be compared to in order to determine statistical significance. This value is typically 0.05. Describes some of the major problems with hypothesis testing
  • The significance level is an arbitrary value.
  • Hypothesis testing is sensitive to sample size.