Christina Clark
October 11, 2018
Lab 8: Data Analysis
OBJECTIVE
Today’s lab will focus on the analysis of data. Students should know how to retrieve data and collaborate effectively by understanding the value of data and accurately interpret it to find correct results. Based off of the previous week’s experiment, student combined data collaboratively on one Excel spreadsheet. With the given inquiry students found the Descriptive Statistics, Histograms, F-Test, and T-Test of their cell counts and assigned assay. After this, student interpreted the results to make a sound judgement on whether to accept or reject the null hypothesis. A null hypothesis is a hypothesis that there is no significant difference between specified populations, and that any observed difference being die to sampling or experimental error.
PROCEDURE
Descriptive Statistics
- Make sure you have access to the ToolPak for Excel. These tools are free and can be added to your Excel software if they are not already included.
- Mac Users: Tools/Excel Add-In/ToolPak. The Data Analysis Box is now on the Data Toolbar or under the Tools Menu.
- PC Users: File/Options/Add-Ins/Analysis ToolPak/Go; Check the Analysis ToolPak box/OK.
On the Data tab, in the Analysis group, you can now click on Data Analysis.
- Organize your data in an Excel Spreadsheet by putting all the data in separate columns giving it its respected header.
- Open the Data Analysis tool under the Data tab. Select Descriptive Statistics and click OK.
- Enter the range of the data you want to be analyzed. Then click the box that is marked Summary Statistics and click OK.
- Do this for each column of data including Control/Treatment cell counts and the data from the vacuole assay.
- Copy and paste the data analysis onto one sheet in order to be able to compare the data without clicking between tabs.
Histograms and Normal Distribution
- On the Data tab, in the Analysis group, click Data Analysis. Select Histogram and click OK.
- Select the Input Range for your data.
- Click in the Bin Range box and select the cells that include the bin numbers.
- Click the Output Range Option button, click in the Output Range box and select the cell where you want the output to be sent.
- Check the box marked Chart Option and click OK.
- Transfer all the data to the data sheet.
The F-Test and Variance
- Open the Data tab, in the Analysis group, click Data Analysis.
- Select F-Test Two-Sample for Variance and click OK.
- Click in the Variable 1 Range box and select the range of your control measurements.
- Click in the Output Range box and select a cell for the output. Click OK.
The t-Test
- On the Data tab, in the Analysis group, click Data Analysis.
- Select the correct t-Test for your data. (Either Paired Two Sample for Means, Two-Sample Assuming Equal Variances) Click OK.
- Click in the Variable 1 Range box and select the range for your Control data.
- Click in the Variable 2 Range box and select the range for your Treatment Data.
- Click in the Hypothesized Mean Difference box and type 0.
- Click the Output Range box and select a cell for your Output. Click OK.
DATA
CONCLUSION
Descriptive Analysis:
The descriptive analysis allows us to get a quick overview of the Mean and Range for each data set.
Histogram Control Cell Count:
Our main interest in the histogram is to visualize the distribution of the data. The distribution of the data varies by increasing, decreasing, and then increasing again as you read the data from left to right. This is not a normal curve to this data most likely due to the small sample sizes. However, we will still use the t-Test for analysis regardless of the distribution.
Histogram Treatment Cell Count:
The distribution of data, in this case, is a normal curve due to its bell curve like shape; increasing and then decreasing from left to right.
Histogram Control Vacuole Formation:
The distribution of data, in this case, is an abnormal curve due to its constant decrease from left to right.
Histogram Treatment Vacuole Formation:
The distribution of data, in this case, is normal due to its increase and the decrease from left to right.
f-Test: Two-Sample for Variances Control and Treatment Cell Count:
If F>F Critical one-tail, we reject the null hypothesis that the variances of the two populations are equal. Therefore, 4.01>1.75 leading to the conclusion that the variances of the populations are not equal causing us to reject the null hypothesis.
f-Test: Two-Sample for Variances Control and Treatment Vacuole Formation:
If F<F Critical one-tail, we accept the null hypothesis that the variances of the two populations are equal. Therefore, 1.09<1.31 leading to the conclusion that the variances of the populations are equal causing us to accept the null hypothesis.
t-Test: Two-Sampling Assuming Unequal Variances Control and Treatment Cell Count:
If -t Critical>t Stat>t Critical two-tail, we reject the null hypothesis and assume that there is a significant difference between the two means. In this case, -1.96<3.02>1.96. Therefore, we do not reject the null hypothesis.
t-Test: Two-Sampling Assuming Equal Variances Control and Treatment Vacuole Formation
If -t Critical>t Stat>t Critical two-tail, we reject the null hypothesis and assume that there is a significant difference between the two means. In this case, -1.9<3.01>1.97. Therefore, we do not reject the null hypothesis.
FUTURE STEPS
In future labs, we hope to further analysis our data collaboratively to determine what the cause of variance is between our control and treatment groups. By using that data we will be able to form a hypothesis as to how our experiment will affect the survival rate and behavior the twine juice will have on the Tetrahymena.