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Feb 20, 2024

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General Biology 1 Honors Fiorella D’Amico January 2024 Measurement Lab: Calculations and Analysis Part B Data Analysis Assays Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Mean Standard Deviation Percent Standard Deviation CG method 100 107 123 97 102 115 107.33 9.79 9.12% GG method 101 109 118 99 104 117 108 8.09 7.49% LG method 98 105 121 103 100 114 106.83 8.89 8.32% The GG method showed the greatest precision with a percent standard deviation of 7.49%. This means that the GG method measurements have the least variability among the three assays. In simpler terms, repeated measurements using the GG method are more likely to be closer to each other compared to the other methods. This lower variability indicates higher precision. As well as no outliers were detected for any of the assays based on the standard 2 standard deviation criterion. We calculated the upper and lower limits for each assay's values using the mean and 2 standard deviations. Since none of the data points fell outside these limits, we can conclude that there are no outliers in this dataset. Part C Data Analysis
CG method: Percent error = ((107.33 - 106.83) / 106.83) * 100% = 0.47% GG method: Percent error = ((108.00 - 106.83) / 106.83) * 100% = 1.09% Compared to the GG method (1.09%), the CG method's absolute percent error is smaller (0.47%). Therefore, the CG method shows better accuracy than the GG method based on the percent error calculations. The CG method shows a lower percent error when comparing the mean values to the accepted value (LG mean) than the GG method. This indicates that CG measurements have a lower deviation from the true value, which leads to higher accuracy. Conclusions When all the numbers are added together, the GG method's measurements are closer to one another because it is more precise and consistent. However, the CG method's average measurements are closer to the true value because it hits the bullseye more accurately. Overall, both approaches are quite good, but they give different priorities. So, the next question we should ask is: Why does one method focus on being consistent while the other focuses on being close to the true value? Understanding why they behave differently can help us pick the best method for different situations.
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