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 inferences about a population based on sample data. It involves formulating a null hypothesis (H0) and an alternative hypothesis (H1). In this context, the null hypothesis might state that 5% of eyeglass wearers purchase online, while the alternative could suggest a different proportion. The goal is to determine whether there is enough evidence to reject the null hypothesis.
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Type I Error
A Type I error occurs when the null hypothesis is incorrectly rejected when it is actually true. In this scenario, it would mean concluding that the proportion of eyeglass wearers who purchase online is different from 5% when, in fact, it is not. This error can lead to false claims about consumer behavior, potentially affecting marketing strategies and business decisions.
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Type II Error
A Type II error happens when the null hypothesis is not rejected when it is false. In this case, it would mean failing to recognize that the proportion of eyeglass wearers who buy online is different from 5%, leading to missed opportunities for businesses to adapt to actual consumer preferences. Understanding this error is crucial for evaluating the effectiveness of the hypothesis test and its implications.
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