A nonprofit consumer organization says that less than 25% of the televisions the organization rated in a recent year have an overall score of 70 or more. In a random sample of 35 televisions the organization rated in a recent year, 23% have an overall score of 70 or more. At α=0.05, can you support the organization’s claim?
Table of contents
- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 56m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion2h 10m
- 9. Hypothesis Testing for One Sample5h 8m
- Steps in Hypothesis Testing1h 6m
- Performing Hypothesis Tests: Means1h 4m
- Hypothesis Testing: Means - Excel42m
- Performing Hypothesis Tests: Proportions37m
- Hypothesis Testing: Proportions - Excel27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions28m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors16m
- 10. Hypothesis Testing for Two Samples5h 37m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- Two Variances and F Distribution29m
- Two Variances - Graphing Calculator16m
- 11. Correlation1h 24m
- 12. Regression3h 33m
- Linear Regression & Least Squares Method26m
- Residuals12m
- Coefficient of Determination12m
- Regression Line Equation and Coefficient of Determination - Excel8m
- Finding Residuals and Creating Residual Plots - Excel11m
- Inferences for Slope31m
- Enabling Data Analysis Toolpak1m
- Regression Readout of the Data Analysis Toolpak - Excel21m
- Prediction Intervals13m
- Prediction Intervals - Excel19m
- Multiple Regression - Excel29m
- Quadratic Regression15m
- Quadratic Regression - Excel10m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA2h 28m
9. Hypothesis Testing for One Sample
Performing Hypothesis Tests: Proportions
Problem 8.2.14
Textbook Question
Testing Claims About Proportions
In Exercises 9–32, test the given claim. Identify the null hypothesis, alternative hypothesis, test statistic, P-value, or critical value(s), then state the conclusion about the null hypothesis, as well as the final conclusion that addresses the original claim. Use the P-value method unless your instructor specifies otherwise. Use the normal distribution as an approximation to the binomial distribution, as described in Part 1 of this section.
Medical Malpractice In a study of 1228 randomly selected medical malpractice lawsuits, it was found that 856 of them were dropped or dismissed (based on data from the Physicians Insurers Association of America). Use a 0.01 significance level to test the claim that most medical malpractice lawsuits are dropped or dismissed. Should this be comforting to physicians?
Verified step by step guidance1
Step 1: Define the null hypothesis (H₀) and the alternative hypothesis (H₁). The null hypothesis represents the claim that the proportion of medical malpractice lawsuits that are dropped or dismissed is equal to 0.5 (i.e., not 'most'). The alternative hypothesis represents the claim that the proportion is greater than 0.5 (i.e., 'most'). Mathematically, H₀: p = 0.5 and H₁: p > 0.5.
Step 2: Calculate the sample proportion (p̂). The sample proportion is given by the formula p̂ = x / n, where x is the number of lawsuits dropped or dismissed (856) and n is the total number of lawsuits (1228). Substitute the values into the formula to find p̂.
Step 3: Compute the test statistic (z). Use the formula z = (p̂ - p₀) / √((p₀(1 - p₀)) / n), where p₀ is the hypothesized population proportion (0.5), p̂ is the sample proportion calculated in Step 2, and n is the sample size (1228). Substitute the values into the formula to calculate z.
Step 4: Determine the P-value. Using the z-value from Step 3, find the corresponding P-value from the standard normal distribution table. Since this is a one-tailed test (H₁: p > 0.5), the P-value represents the area to the right of the calculated z-value.
Step 5: Compare the P-value to the significance level (α = 0.01) and make a conclusion. If the P-value is less than or equal to 0.01, reject the null hypothesis (H₀) and conclude that most medical malpractice lawsuits are dropped or dismissed. Otherwise, fail to reject H₀. Finally, interpret the result in the context of the problem and address whether this should be comforting to physicians.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Hypothesis Testing
Hypothesis testing is a statistical method used to make decisions about a population based on sample data. It involves formulating two competing hypotheses: the null hypothesis (H0), which represents a statement of no effect or no difference, and the alternative hypothesis (H1), which indicates the presence of an effect or difference. The goal is to determine whether there is enough evidence in the sample data to reject the null hypothesis in favor of the alternative.
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Step 1: Write Hypotheses
P-value
The P-value is a measure that helps determine the strength of the evidence against the null hypothesis. It represents the probability of obtaining a test statistic at least as extreme as the one observed, assuming the null hypothesis is true. A smaller P-value indicates stronger evidence against the null hypothesis, and if it is less than the predetermined significance level (e.g., 0.01), the null hypothesis is rejected.
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Step 3: Get P-Value
Significance Level
The significance level, often denoted as alpha (α), is the threshold used to decide whether to reject the null hypothesis. It represents the probability of making a Type I error, which occurs when the null hypothesis is incorrectly rejected. In this case, a significance level of 0.01 means there is a 1% risk of concluding that a claim is true when it is actually false, guiding the decision-making process in hypothesis testing.
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Step 4: State Conclusion Example 4
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