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Ch. 7 - Hypothesis Testing with One Sample
Larson - Elementary Statistics: Picturing the World 8th Edition
Larson8th EditionElementary Statistics: Picturing the WorldISBN: 9780137493470Non è quello che usi tu?Cambia libro di testo
Capitolo 7, Problema 7.3.18

In Exercises 13–18, test the claim about the population mean μ at the level of significance α. Assume the population is normally distributed.
Claim: μ≠52,200; α=0.05. Sample statistics: x_bar=53,220, s=2700, n=34

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Step 1: State the null hypothesis (H₀) and the alternative hypothesis (H₁). The null hypothesis is H₀: μ = 52,200, and the alternative hypothesis is H₁: μ ≠ 52,200. This is a two-tailed test because the claim specifies 'not equal to.'
Step 2: Identify the test statistic to use. Since the population standard deviation is not provided and the sample size is relatively small (n = 34), use the t-test. The formula for the t-test statistic is: t = (x̄ - μ) / (s / √n), where x̄ is the sample mean, μ is the population mean under H₀, s is the sample standard deviation, and n is the sample size.
Step 3: Calculate the degrees of freedom (df) for the t-distribution. The degrees of freedom are given by df = n - 1. In this case, df = 34 - 1 = 33.
Step 4: Determine the critical t-value(s) for a two-tailed test at the significance level α = 0.05. Use a t-distribution table or statistical software to find the critical t-values corresponding to df = 33 and α/2 = 0.025 in each tail.
Step 5: Compare the calculated t-test statistic to the critical t-values. If the absolute value of the t-test statistic exceeds the critical t-value, reject the null hypothesis H₀. Otherwise, fail to reject H₀. Additionally, you can calculate the p-value and compare it to α to make the decision.

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Hypothesis Testing

Hypothesis testing is a statistical method used to make decisions about a population based on sample data. It involves formulating a null hypothesis (H0) and an alternative hypothesis (H1). In this case, the null hypothesis states that the population mean μ equals 52,200, while the alternative hypothesis claims that it does not. The goal is to determine whether there is enough evidence to reject the null hypothesis at a specified significance level.
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Step 1: Write Hypotheses

Significance Level (α)

The significance level, denoted as α, is the threshold for determining 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 scenario, α is set at 0.05, indicating a 5% risk of concluding that a difference exists when there is none. This level helps to control the likelihood of false positives in hypothesis testing.
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Step 4: State Conclusion Example 4

Confidence Interval

A confidence interval is a range of values derived from sample statistics that is likely to contain the population parameter with a certain level of confidence. In the context of hypothesis testing, it can be used to assess whether the population mean falls within a specified range. If the hypothesized mean (52,200) lies outside the confidence interval calculated from the sample data, it provides evidence against the null hypothesis.
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Introduction to Confidence Intervals
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