ARandomization Distribution for Arsenic in Chicken Arestaurant chain is measuring the levels of arsenic in chicken from its suppliers. The question is whether there is evidence that the mean level of arsenic is greater than 80 ppb , so we are testing \( H_{0}: \mu=80 \mathrm{vs} H_{a}: \mu>80 \), where \( \mu \) represents the average level of arsenic in all chicken from a certain supplier. It takes money and time to test for arsenic so samples are often small. A sample of \( n=6 \) chickens from one supplier is tested, and the resulting sample mean is \( \bar{x}=91 \). Subtracting 11 from the sample data to move the mean down to the null mean of \( \bar{x}=80 \) results in the following data: Click here to access StatKey. Part 1 (a) Use StatKey or other technology to create the randomization distribution for this test. Find the \( p \)-value. Round your answer to three decimal places. Use 1000 samples. p-value \( = \) i
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Beyond the Answer
When you're dealing with small sample sizes, especially in tests for things like arsenic levels in chicken, randomization methods like those in StatKey can be a game-changer! In your specific case, you can create a randomization distribution by simulating many samples under the null hypothesis. With 1,000 samples, you'll gain a solid sense of how extreme your observed sample mean (91 ppb) is compared to what we would expect if the true mean were indeed 80 ppb. The magic of this process comes as you analyze the random samples' means and see how many of them surpass your sample mean of 91. The \( p \)-value you calculate from this distribution corresponds to the proportion of simulated means greater than or equal to your observed mean. If your result ends up being typically less than 0.05, congratulations, you've got evidence to suggest a mean arsenic level above 80 ppb—definitely a cause for concern in the kitchen! Take a moment to run that test and round your findings to three decimal places for clarity. Happy analyzing!