Best Method of Analyzing Experimental Percent Error

In summary, the experimental data suggests that there is a weak positive correlation between molarity and percent yield.
  • #1
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Homework Statement


I have performed a chemistry experiment where I analyzed how the diluting of two reactants with the same volume and initial concentration impacts the percent yield of a precipitate product. Thus my IV was concentration and my DV was percent yield. My question is what is the correct/most effective method of analyzing the percent error of my experiment?

Homework Equations


percent yield = experimental yield / theoretical yield * 100%

percent error = (|theoretical value - experimental value|) / theoretical value * 100%

The Attempt at a Solution



I have considered two prospects:

1 . I plot concentration of reactants versus experimental yield and linearize if necessary, finding the slope of the graph. I then plot concentration versus theoretical yield and repeat the same process. I use the experimental slope as my experimental value and the theoretical slope as my theoretical value in the percent error calculation. The problem with this is that there is no consideration of the dependent variable, which is percent yield.

2. I find the averages of all experimental masses for every IV, (averages of averages), getting one number representing the average of all my experimental data, and compare this to the averages of all my theoretical masses (calculated through stoich) in the percent yield equation.

Without examining the root mathematical causes of the variance in percent yield due to changes in concentration, I don't think I can reasonably calculate the percent of error. If this is the case, I was wondering if anyone could point me in the right direction.

Any help would be greatly appreciated!
 
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  • #2
It depends on what you want to do with that number.

Do you expect that all experimental values are too low/high in the same (relative) way?
Do you expect yield/concentration to be linear?

If both answers are "yes", method 1 is probably a good idea.
"yes&no": 2 is probably a good approach, but a weighted average could be better, where the weight takes into account the precision of the measurement.
If the answer to question 1 is "no", then what do you want to quantify with a single number?
 
  • #3
My results when I graphed molarity against percent yield were a weak positive correlation. The slope was 51 exactly however the correlation was 0.8978. I will consider both prospects as I think the percent yield would increase linearly with molarity increases but I haven't been able to find any information online that supports this claim.
 

What is experimental percent error and why is it important?

Experimental percent error is the difference between the measured value of an experimental quantity and the accepted value, expressed as a percentage of the accepted value. It is important because it allows scientists to evaluate the accuracy of their experimental results and identify potential sources of error.

What are the different methods of calculating experimental percent error?

The two most commonly used methods are absolute percent error and relative percent error. Absolute percent error is calculated by taking the absolute value of the difference between the measured and accepted values, divided by the accepted value, and multiplied by 100. Relative percent error is calculated by taking the absolute percent error and dividing it by the accepted value, then multiplying by 100.

Which method of calculating experimental percent error is considered the "best"?

Neither method is considered inherently "better" than the other. The choice of method depends on the specific situation and what the scientist is trying to determine. Absolute percent error is useful for comparing the magnitude of errors between different experiments, while relative percent error is more useful for comparing the accuracy of different measurements of the same quantity.

What are some common sources of error that can contribute to experimental percent error?

There are many potential sources of error in an experiment, including human error, instrument limitations, environmental factors, and systematic errors. It is important for scientists to carefully control and account for these sources of error in order to minimize their impact on the accuracy of their results.

How can experimental percent error be minimized or reduced?

To minimize experimental percent error, scientists can take steps such as using precise and accurate instruments, carefully following experimental procedures, repeating measurements multiple times, and identifying and controlling for potential sources of error. Additionally, as experimental techniques and technologies continue to advance, the accuracy of measurements and calculations can be improved, leading to lower overall percent error.

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