Investigate the cause of the error

Oct 13, 2025 by pynkocean.com 52 views

Introduction

In the world of quality control, accuracy is paramount. A quality control manager is responsible for ensuring that products, including digital scales, meet the required standards. In this scenario, a quality control manager is inspecting four digital scales to determine if they accurately reflect a weight of 0 ounces. The table below shows the weight displayed on four empty scales.

Table: Empty Scale Weights

Scale Number	Weight Displayed (oz)
1	0.00
2	0.00
3	0.01
4	0.00

The Problem

At first glance, it may seem that all four scales are accurate, displaying a weight of 0 ounces. However, upon closer inspection, we notice that Scale 3 displays a weight of 0.01 ounces, which is not exactly 0 ounces. This raises a question: what is the probability that the scales are accurate?

Probability of Accuracy

To determine the probability of accuracy, we need to consider the number of possible outcomes and the number of favorable outcomes. In this case, there are four possible outcomes (the weight displayed on each scale), and only one of them is accurate (0 ounces).

Calculating Probability

The probability of accuracy can be calculated using the formula:

P(A) = Number of favorable outcomes / Total number of possible outcomes

In this case, the number of favorable outcomes is 1 (the weight displayed on Scale 1 is 0 ounces), and the total number of possible outcomes is 4 (the weight displayed on each of the four scales).

P(A) = 1/4 = 0.25

Interpretation

The probability of accuracy is 0.25, which means that there is a 25% chance that the scales are accurate. This is a relatively low probability, and it suggests that there may be a problem with the scales.

Hypothesis Testing

To further investigate the accuracy of the scales, we can use hypothesis testing. The null hypothesis (H0) is that the scales are accurate, and the alternative hypothesis (H1) is that the scales are not accurate.

Null Hypothesis (H0)

H0: The scales are accurate, and the weight displayed on each scale is 0 ounces.

Alternative Hypothesis (H1)

H1: The scales are not accurate, and the weight displayed on each scale is not 0 ounces.

Test Statistic

To test the null hypothesis, we can use a test statistic, such as the mean of the weights displayed on the scales. If the mean is close to 0 ounces, we can reject the null hypothesis and conclude that the scales are not accurate.

P-Value

The p-value is the probability of observing a test statistic at least as extreme as the one we obtained, assuming that the null hypothesis is true. If the p-value is less than a certain significance level (e.g., 0.05), we can reject the null hypothesis and conclude that the scales are not accurate.

Conclusion

In conclusion, the probability of accuracy is 0.25, which suggests that there may be a problem with the scales. Using hypothesis testing, we can further investigate the accuracy of the scales and determine whether the null hypothesis can be rejected.

Recommendations

Based on the analysis, we recommend the following:

Re-calibrate the scales: The scales may need to be re-calibrated to ensure that they are accurate.
Investigate the cause of the error: The cause of the error should be investigated to prevent it from happening again in the future.
Implement quality control measures: Quality control measures should be implemented to ensure that products meet the required standards.

Future Research Directions

Future research directions may include:

Investigating the effect of temperature on the scales: The effect of temperature on the scales should be investigated to determine whether it affects their accuracy.
Comparing the accuracy of different scales: The accuracy of different scales should be compared to determine which one is the most accurate.
Developing a new quality control method: A new quality control method should be developed to ensure that products meet the required standards.

Limitations of the Study

The study has several limitations, including:

Small sample size: The sample size is small, which may not be representative of the population.
Limited data: The data is limited, which may not be sufficient to draw conclusions.
Assumptions: The study assumes that the scales are accurate, which may not be the case.

Future Studies

Future studies should aim to address the limitations of the current study and provide a more comprehensive understanding of the accuracy of digital scales.