Calculating the Test Statistic

Oct 13, 2025 by miodeya.com 57 views

Introduction

As a consumer advocacy group, it is essential to ensure that the products we consume are safe and meet the advertised content. Recently, a popular brand of cereal has been under scrutiny for its sugar content. The advertised content of 10 grams per serving has raised concerns among consumers. In this article, we will explore the statistical analysis required to determine if the average amount of sugar in this cereal exceeds the advertised content.

The Problem

The consumer advocacy group has collected a random sample of 30 boxes of cereal from various stores. Each box has been analyzed for its sugar content, and the results are as follows:

Box Number	Sugar Content (g)
1	12.5
2	9.2
3	11.1
4	10.5
5	13.8
...	...
30	8.9

The group wants to determine if the average sugar content of the cereal exceeds the advertised content of 10 grams per serving.

Hypothesis Testing

To address this problem, we will use hypothesis testing. The null hypothesis (H0) is a statement of no effect or no difference, while the alternative hypothesis (H1) is a statement of an effect or a difference. In this case, the null hypothesis is that the average sugar content of the cereal is equal to or less than 10 grams per serving, while the alternative hypothesis is that the average sugar content exceeds 10 grams per serving.

Null Hypothesis (H0)

H0: μ ≤ 10, where μ is the population mean sugar content.

Alternative Hypothesis (H1)

H1: μ > 10, where μ is the population mean sugar content.

Significance Test

The appropriate significance test for this problem is a one-sample t-test. This test is used to determine if the mean of a sample is significantly different from a known population mean.

One-Sample t-Test

The one-sample t-test is a parametric test that assumes the data follows a normal distribution. The test statistic is calculated as follows:

t = (x̄ - μ) / (s / √n)

where x̄ is the sample mean, μ is the known population mean, s is the sample standard deviation, and n is the sample size.

Calculating the Test Statistic

To calculate the test statistic, we need to calculate the sample mean, sample standard deviation, and sample size.

Sample mean (x̄) = 11.35
Sample standard deviation (s) = 1.53
Sample size (n) = 30

Now, we can calculate the test statistic:

t = (11.35 - 10) / (1.53 / √30) t = 1.35 / 0.26 t = 5.19

Interpretation of the Test Statistic

The test statistic (t) is 5.19, which is greater than the critical value of 1.96 for a one-tailed test with a significance level of 0.05. This indicates that the sample mean is significantly different from the known population mean.

Conclusion

Based on the one-sample t-test, we can conclude that the average sugar content of the cereal exceeds the advertised content of 10 grams per serving. This result is statistically significant, and we can reject the null hypothesis.

Recommendations

Based on the results of this analysis, the consumer advocacy group recommends that the manufacturer of the cereal re-evaluate its sugar content and consider reducing the amount of sugar in each serving.

Limitations

This analysis has several limitations. The sample size is relatively small, and the data may not be representative of the entire population. Additionally, the analysis assumes that the data follows a normal distribution, which may not be the case.

Future Research

Future research should focus on collecting a larger sample size and using more robust statistical methods to analyze the data. Additionally, the manufacturer of the cereal should consider conducting its own analysis to determine the sugar content of its product.

References

[1] "One-Sample t-Test." Wikipedia, Wikimedia Foundation, 2023.
[2] "Hypothesis Testing." Wikipedia, Wikimedia Foundation, 2023.

Appendix

The data used in this analysis is available in the appendix. The data includes the sugar content of 30 boxes of cereal.

Box Number	Sugar Content (g)
1	12.5
2	9.2
3	11.1
4	10.5
5	13.8
...	...
30	8.9

Introduction

In our previous article, we explored the statistical analysis required to determine if the average amount of sugar in a popular brand of cereal exceeds the advertised content. We used a one-sample t-test to analyze the data and found that the average sugar content of the cereal exceeds the advertised content of 10 grams per serving. In this article, we will answer some frequently asked questions related to this analysis.

Q: What is the purpose of hypothesis testing?

A: The purpose of hypothesis testing is to determine if there is a statistically significant difference between a sample mean and a known population mean.

Q: What is the null hypothesis (H0)?

A: The null hypothesis (H0) is a statement of no effect or no difference. In this case, the null hypothesis is that the average sugar content of the cereal is equal to or less than 10 grams per serving.

Q: What is the alternative hypothesis (H1)?

A: The alternative hypothesis (H1) is a statement of an effect or a difference. In this case, the alternative hypothesis is that the average sugar content of the cereal exceeds 10 grams per serving.

Q: What is the significance level (α)?

A: The significance level (α) is the probability of rejecting the null hypothesis when it is true. In this case, we used a significance level of 0.05.

Q: What is the critical value of the t-distribution?

A: The critical value of the t-distribution is the value of the t-statistic that corresponds to the significance level. In this case, the critical value is 1.96 for a one-tailed test.

Q: What is the test statistic (t)?

A: The test statistic (t) is a measure of the difference between the sample mean and the known population mean. In this case, the test statistic is 5.19.

Q: What does the test statistic (t) indicate?

A: The test statistic (t) indicates that the sample mean is significantly different from the known population mean. In this case, the test statistic is greater than the critical value, indicating that the average sugar content of the cereal exceeds the advertised content.

Q: What are the limitations of this analysis?

A: The limitations of this analysis include a relatively small sample size and the assumption that the data follows a normal distribution.

Q: What are the recommendations for future research?

A: The recommendations for future research include collecting a larger sample size and using more robust statistical methods to analyze the data.

Q: What is the conclusion of this analysis?

A: The conclusion of this analysis is that the average sugar content of the cereal exceeds the advertised content of 10 grams per serving. This result is statistically significant, and we can reject the null hypothesis.

Q: What are the implications of this analysis?

A: The implications of this analysis are that the manufacturer of the cereal should re-evaluate its sugar content and consider reducing the amount of sugar in each serving.

Q: What are the references for this analysis?

A: The references for this analysis include [1] "One-Sample t-Test." Wikipedia, Wikimedia Foundation, 2023, and [2] "Hypothesis Testing." Wikipedia, Wikimedia Foundation, 2023.

Q: What is the appendix of this analysis?

A: The appendix of this analysis includes the data used in the analysis, which includes the sugar content of 30 boxes of cereal.

Box Number	Sugar Content (g)
1	12.5
2	9.2
3	11.1
4	10.5
5	13.8
...	...
30	8.9

Note: The data is fictional and used only for illustrative purposes.