Figure of Experimental Treatment vs. Control Data

Purpose:

To produce a clear and informative graph that conveys the message lying behind your data.

Procedure:

In making this figure, I first opened up my Excel file that contained my two columns of data for the control and the experiment. To end up with a concise and representable graph, I took the average of my control and treatment data first. I then selected both of the columns, went to the ‘Insert’ tab, selected ‘Recommended Charts’ and chose the graph that would best display my data. After I chose my chart, I was able to type in a specific title and my x and y axes by going to ‘Chart Design’, clicking ‘Add Chart Design’ and clicking ‘Axis Titles’. I put labeled my x-axis as ‘Experimental Groups’ and my y-axis as ‘Cells/mL of Tetrahymena’. I also incorporated the standard error in the graph to show that there was no difference between the means of the control and the treatment. I did this by, again, pushing the ‘Chart Design’ tab and ‘Add Chart Design’ and then choosing ‘Error Bars’ and standard errors. Lastly, I right-clicked on the graph and pushed ‘Format Data Point’, which allowed me to change the color and patterns of the different columns to differentiate them both.

Data & Observations:

This graph represents the average cells/mL of the control and the treatment groups of Tetrahymena. The control group, in this graph, is blue and it has small dots to distinguish it as well, while the treatment group is represented by green diagonal lines. The line above each column represents the standard error of each group, which allows one to be able to compare the graphs and determine whether there is a true difference between the data.

Conclusion:

In this lab, I went through the process of creating a figure to compare my treatment and control data. After editing and looking at several charts, I found that a bar graph, showcasing with the means of the control and experimental group, was the most concise figure to make. Incorporating the standard error in the graph also revealed that there was no real difference between the two groups. This is justified by the p-value, because the p-value is greater than 0.05, revealing that there was no real difference between the means, thus supporting the null hypothesis. This graph was clear, concise and correct, and would allow both the researchers and readers to easily understand the information on the cells/mL in the treatment versus the control. This figure will be essential to the research paper, because it will allow readers and researchers to understand and interpret the data collected in the lab experiment.