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\( 1 \leftarrow \quad \begin{array}{l}\text { In a certain survey, } 521 \text { people chose to respond to this question: "Should passwords be replaced with biometric security (fingerprints, } \\ \text { etc)?" Among the respondents, } 51 \% \text { said "yes." We want to test the claim that more than half of the population believes that passwords } \\ \text { should be replaced with biometric security. Complete parts (a) through (d) below. } \\ \text { (d) Are any of the three requirements violated? Can a test about a population proportion using the normal approximation method } \\ \text { be used? } \\ \text { A. All of the conditions for testing a claim about a population proportion using the normal approximation method are satisfied, so } \\ \text { the method can be used. } \\ \text { B. The conditions np } \geq 5 \text { and nq } \geq 5 \text { are not satisfied, so a test about a population proportion using the normal approximation } \\ \text { method cannot be used. } \\ \text { C. One of the conditions for a binomial distribution are not satisfied, so a test about a population proportion using the normal } \\ \text { approximating method cannot be used. } \\ \text { D. The sample observations are not a random sample, so a test about a population proportion using the normal approximating } \\ \text { method cannot be used. }\end{array} \) (t)

Ask by Herrera Nunez. in the United States
Mar 13,2025

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The sample is not a random sample, so the normal approximation method cannot be used for testing the claim.

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To determine whether you can use the normal approximation for a test about a population proportion, you check the conditions \( np \geq 5 \) and \( nq \geq 5 \), where \( n \) is the sample size, \( p \) is the sample proportion, and \( q = 1 - p \). In this case, with 521 respondents and 51% voting "yes," you would calculate \( np = 521 \times 0.51 = 266.71 \) and \( nq = 521 \times 0.49 = 254.29 \). Since both values are greater than 5, you can confidently use the normal approximation method! Additionally, remember that using a proper random sampling technique is crucial in statistical tests. If the sample isn't random, it could introduce bias, affecting the validity of your test results. Always ensure the sampling comes from a diverse and representative population to maintain accuracy in your conclusions!

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