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Arsenic in Chicken Data 4.5 on page 228 introduces a situation in which a restaurant 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 \(H_{0}: \mu=80\) 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. Suppose \(n=6\) chickens from one supplier are tested, and the levels of arsenic (in ppb) are: \(68, \quad 75\) 81, \(\quad 93\) 134 (a) What is the sample mean for the data? (b) Translate the original sample data by the appropriate amount to create a new dataset in which the null hypothesis is true. How do the sample size and standard deviation of this new dataset compare to the sample size and standard deviation of the original dataset? (c) Write the six new data values from part (b) on six cards. Sample from these cards with replacement to generate one randomization sample. (Select a card at random, record the value, put it back, select another at random, until you have a sample of size \(6,\) to match the original sample size.) List the values in the sample and give the sample mean. (d) Generate 9 more simulated samples, for a total of 10 samples for a randomization distribution. Give the sample mean in each case and create a small dotplot. Use an arrow to locate the original sample mean on your dotplot.

Step by step solution

01

Calculate the Sample Mean

First, sum all the data values (68, 75, 81, 93, 134) and then divide by the number of data values (n=6) to calculate the sample mean. The formula used for calculating the sample mean is \( \bar{x} = \frac{\sum x}{n} \)

02

Translate the Original Sample Data

Subtract the sample mean from 80 (value of null hypothesis) to calculate the offset. Then, subtract this offset from each of the sample data values to produce the new dataset. The sample size remains the same because the translation doesn't involve removing or adding data, and the standard deviation remains the same because it is a measure of dispersion, not position.

03

Generate One Randomization Sample

Draw six cards at random, each time replacing the drawn card before drawing the next. Record the values to create the new sample and then calculate the sample mean of the new sample.

04

Generate More Simulated Samples for Randomization

Repeat the procedure from the previous step to generate nine more samples. After each simulation, calculate and record the sample mean.

05

Create a Dotplot

Plot the sample means obtained from the ten randomization samples on a dotplot. Indicate the original sample mean with an arrow.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Sample Mean

The sample mean is a useful statistic that provides an average of a set of data points. It is particularly helpful in various fields, including statistics and research, as it gives an overview of the data's central tendency. To calculate the sample mean, we need to sum up all data values and then divide this total by the number of values. For example, if we have arsenic levels as 68, 75, 81, 93, and 134 in ppb from tested chickens, we find the sample mean by computing:

• Sum the data: 68 + 75 + 81 + 93 + 134
• Divide the sum by the number of values, which is 5, rather than 6 as listed in the original step, indicating a slip, since the data provided has a count of five, expecting correction.

This will yield the average arsenic level, which can then be compared to the hypothesized value to assess significance.

Randomization Sample

Randomization sampling is a method used to simulate the process of random selection and is a key part of hypothesis testing. This involves creating samples from a given dataset by "sampling with replacement," meaning after a data point is selected, it is put back and can possibly be chosen again.
This method helps create a comparison distribution that tells us, under the null hypothesis, how sample means would behave due to random chance. By sampling multiple times, we can build up a randomization distribution:

• Select a card at random from the new data set (modified to match the null hypothesis), note its value.
• Replace the card, ensuring it can be selected again.
• Repeat this process until you've reached the desired sample size, in this case, 6 cards to mirror the original sample size.

By creating multiple randomization samples, we can better understand the variability that might naturally occur in sample means.

Standard Deviation

Standard deviation serves as a measure of how spread out numbers are in a data set. In statistical analysis, understanding how dispersed data points are provides insights into variability and consistency within the dataset. For the arsenic levels, acknowledging their spread helps anticipate differences and similarities with the hypothesized mean.
Standard deviation is calculated by determining how far each data point is from the mean, squaring these distances, finding their average, and then taking the square root of this average. This calculation represents average deviation from the sample mean:

• Data points close to the mean result in a smaller standard deviation.
• Larger deviations indicate more variability in the sample.

It's essential to remember that even when data is adjusted or translated (as in step 2 of translating the original data), the standard deviation remains unaffected, as it is purely about the spread of the data, not its position.