2026 WGU Applied-Probability-and-Statistics Accurate Valid Practice Materials

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WGU Applied-Probability-and-Statistics Exam Syllabus Topics:

SectionObjectives
Descriptive Statistics- Data summarization
  • 1. Measures of variability (range, variance, standard deviation)
    • 2. Measures of central tendency (mean, median, mode)
      - Data visualization
      • 1. Histograms and frequency distributions
        • 2. Box plots and interpretation
          Regression and Correlation- Relationship analysis
          • 1. Simple linear regression basics
            • 2. Correlation coefficient interpretation
              Probability Distributions- Continuous distributions
              • 1. Standard normal and z-scores
                • 2. Normal distribution
                  - Discrete distributions
                  • 1. Poisson distribution (introductory use cases)
                    • 2. Binomial distribution
                      Statistical Inference- Estimation
                      • 1. Confidence intervals for means and proportions
                        - Hypothesis testing
                        • 1. t-tests and z-tests (basic application)
                          • 2. Null and alternative hypotheses
                            Probability- Fundamental probability concepts
                            • 1. Events and sample spaces
                              • 2. Conditional probability and independence
                                - Probability rules
                                • 1. Addition and multiplication rules
                                  • 2. Bayes’ theorem (introductory level)

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                                    Applied-Probability-and-Statistics Exam Lab Questions | Applied-Probability-and-Statistics Test Valid

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                                    WGU Applied Probability and Statistics (FZO1 C955) Sample Questions (Q74-Q79):

                                    NEW QUESTION # 74
                                    Type I error = ?

                                    Answer: A

                                    Explanation:
                                    A Type I error occurs when a statistical test rejects the null hypothesis even though the null hypothesis is actually true. This is often described as a false positive. In practical terms, the test concludes that there is an effect, difference, or relationship when none truly exists under the null condition. The probability of committing a Type I error is denoted by #, the significance level, often set at 0.05. Option B describes a Type II error, where the test fails to reject a false null hypothesis. Option C is not an error at all. Option D is too general; Type I error is not a data-entry issue but a formal decision error in hypothesis testing. The defining phrase is "reject H# when true." Study Guide references/topics: hypothesis testing, Type I error, null hypothesis, significance level.


                                    NEW QUESTION # 75
                                    Conditional probability formula:

                                    Answer: C

                                    Explanation:
                                    Conditional probability measures the probability that event A occurs given that event B has already occurred.
                                    The formula is P(A|B) = P(A and B)/P(B), assuming P(B) is greater than zero. The denominator P(B) restricts the sample space to cases where B occurs, and the numerator P(A and B) counts cases where both A and B occur within that restricted space. Option B is incorrect because it divides P(A) by P(B) without requiring overlap between A and B. Option C reverses the conditioning and would relate to P(B|A) only if the numerator were P(A and B). Option D applies to independent events when finding P(A and B), not to conditional probability in general. The vertical bar in P(A|B) is read as "given," and it signals that the denominator should be the probability of the given event. Study Guide references/topics: conditional probability, joint probability, event intersection, probability rules.


                                    NEW QUESTION # 76
                                    Histogram vs bar chart:

                                    Answer: B


                                    NEW QUESTION # 77
                                    95% CI = 50 ± 2. SE = ?

                                    Answer: A

                                    Explanation:
                                    A confidence interval has the structure estimate ± margin of error. The margin of error is calculated as critical value × standard error. For a 95% confidence interval using the normal approximation, the critical value is approximately 1.96, often rounded to 2 in introductory settings. The interval is given as 50 ± 2, so the margin of error is 2. Using the approximate 95% critical value of 2, we solve 2 = 2 × SE, giving SE = 1. More exactly, using 1.96 gives SE = 2/1.96 # 1.02, which rounds to 1. Option B confuses the margin of error with the standard error. Option C is too large, and option D would produce a margin of error near 1 under a 95% critical value. The correct standard error is approximately 1. Study Guide references/topics: confidence intervals, standard error, critical value, margin of error.


                                    NEW QUESTION # 78
                                    Independent events: P(A|B) = ?

                                    Answer: B

                                    Explanation:
                                    For independent events, the occurrence of one event does not change the probability of the other. Conditional probability P(A|B) means the probability of A occurring given that B has occurred. If A and B are independent, knowing B occurred gives no new information about A. Therefore, P(A|B) = P(A). This is one of the defining properties of independence. For example, if a coin toss and a die roll are independent, knowing the die landed on 4 does not change the probability that the coin landed heads; it remains 1/2. Option B reverses the event being measured. Option C would imply that A becomes impossible after B, which describes a different situation. Option D is an addition expression and does not represent conditional probability. The correct relationship is that the conditional probability equals the original probability when events are independent. Study Guide references/topics: independent events, conditional probability, probability rules, event relationships.


                                    NEW QUESTION # 79
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