Lab 1 - Micropipetting Post Laboratory Questions PDF

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Post laboratory questions with data included from the first in person lab on micropipetting. ...

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Post Lab 1 Questions Pipetting 1. Using all the class data, put the measured mean and standard deviation for each pipette (p1000, p200, p10, serological) in a table using this format: mean +/- SD In the next column, put the expected value from each protocol In the third column, put the difference between the means (measured – expected) Make sure the table has all the rows and columns labeled with the appropriate units, if necessary (7)

2. Which pipette was the closest to the expected value? (1) The p1000 was closest to the expected value with a percent difference of 0.04%. 3. Looking at the accuracy and precision data of all the pipettes, list the pipettes in order of best to worst for accuracy and then for precision (4) Best to worst accuracy: p1000, p200, serological, p10 Best to worst precision: p10, p200, p1000, serological 4. For each pipette you used, which way of pipetting the prescribed volume was most accurate and what conclusion does this lead you to? (3) The class data shows that pipetting the entire volume led to a more accurate measurement. This led me to believe that pipetting the desired volume in one turn is ideal to eliminate errors that could arise when pipetting the total volume in smaller increments. By doing this in one turn, there is less room for error to be made that leads to a less accurate measurement. 5. Refer to your analysis from question #4 If you were training a new lab member, what recommendation would you make about using pipettes accurately? (1) If I were training a new lab member, I would recommend that they understand which pipet to use based on the volume they are attempting to measure. It is important to use the proper pipet to ensure the entire volume is dispensed. When they are adjusting the pipet to the proper volume, I would recommend they measure the entire volume in one turn rather than adding smaller amounts to reach the desired value. 6. What is standard deviation and how much of the data by percent is within 1 standard deviation? (2) Standard deviation is a measurement that shows the spread or the variability of the values within a set of data. The data within one standard deviation of the mean refers to about 68% of the data set. Most of the data is included in 1 standard deviation. 7. Why did we use the Excel function “stdev.s” and not the other version of the function “stdev.p”? (1) The function “stdev.p” is used when the data used represents the entire population. Thus we used “stdev.s” as the data is a sample of the entire population. The calculations for this lab mandates each sample to be evaluated independently.

8. The manufacturer indicated that the micro pipettes (p1000, p200, p10) have an accuracy of 0.8% and a precision of 0.15% How did your class data compare to the manufacturers report, and if the values are not exactly the same, list three reasons that contributed to that difference? (6) The class accuracies of the p1000, p200, and p10 pipette were -0.0401%, -0.7529%, and -8.7719%. The class precisions of the p1000, p200, and p10 pipette were 2.0089%, 3.8142%, and 19.9852%. If you take the absolute value of the accuracies calculated from the class data the p1000 and p200 were lower than the manufacturer’s accuracy, however, the p10 was quite a bit higher. This may be due to the fact that the class data set was quite small, thus an outlier has a large impact on the data. Additionally, it is possible the amount of liquid was not measured exactly, leading to another source of variation. Another possible reason for the differences in measurement may be due to the catching of air bubbles within the pipette which would then lead to a reduction in weight. The precision from the class data was much higher than the manufacturer's indicated value. The physical pipettes used in this laboratory may have had minor inherent differences that led to slightly shifted data. Pipet

Manufacturer Accuracy

Class Accuracy

Manufacturer Precision

Class Precision

p1000

0.8%

-0.0401%

0.15%

2.0889%

p200

0.8%

-0.7259%

0.15%

3.8142%

p10

0.8%

-8.7719%

0.15%

19.9852%

Osmosis and Diffusion 1. Obtain the data from the shared document on Canvas and copy it into your own Excel file a. b. c.

d. e.

Create a line graph of the class mean for “% change in weight” at each molarity Add a second line of just your data Add error bars to each point showing the standard deviation for the graph of the Means i. See Excel help and PDF on "How to add error bars in Excel " Label the graph with a title and axis labels w/units and key Once you make the graph in Excel, insert it into the conclusion question (7)

Figure 1: Molarity vs. Percent Change in Weight with Error Bars

2. What do the error bars tell us about the experiment in general? (2) Error bars denote the range of error within a specific point on the data set. 3. How does your data compare to the class average? (2) I was not present for the lab however using a classmates data. The majority of the values are closely related however at 0.3 M the Class Percentage Change in Weight is much lower than the Individual value. 4. Explain two ways you could have improved your data (4) Our data may have been improved if we were to test the unknown solution across a larger range of solutions with known molarities. Another way we could’ve improved our data is by ensuring the dialysis tubing was properly clipped. 5. List two other possible sources of error in this experiment (2) Possible sources of error may have occurred due to not tarring the scale properly or not waiting the entire 45 minutes to reach equilibrium. There may also have been varying weights of the dialysis clips. If air bubbles were present when the dialysis tube was closed, the data may have been altered. 6. In Excel, add a linear best-fit line to the class mean graph Find the x-intercept, where y=0, and that should be the molarity of your unknown solution What molarity was the unknown solution? (2) The unknown solution had a molarity of about 0.47 moles. This was found using the equation from the linear best-fit line. The equation for the linear trendline was: y = − 17.327 x + 8.0742 .

7. Does the value of the unknown solution match your hypothesis made in lab? Why or why not? (2) While I was not present for the lab, I hypothesized that the solution would create a hypertonic environment. I thought that there would be a higher molarity as well. This is supported by the data as the calculated molarity is 0.47  moles, higher than the molarity of the other solutions....