Chapter 2. EVOLUTION III—OVERVIEW

Learning Objectives

General Purpose

Conceptual

• Gain experience presenting the results and conclusions of an experiment to other individuals.

Now that you have completed the data collection for your experiment, you need to analyze your data. What does it mean for your data to be accurate or precise? Although the words accuracy and precision are often used as though they are synonymous, they are not. Accuracy refers to the closeness of a measured or calculated value to its “true” value. An experiment will have a single, exact outcome. If your recorded value for the experiment was near this value, it was very accurate. An accurate reading can be the result of good experimental technique or it could be the result of random chance. You could have measured the outcome incorrectly, but then also recorded the value wrong, with these two errors canceling each other out and therefore producing a very accurate result. Precision is the closeness of repeated measurements to one another. Precise measurements are not necessarily accurate, but in general a series of very precise measurements will also be very accurate because it is less likely for random events to occur in exactly the same way multiple times. This is partially the reason experiments are usually repeated many times. It allows the investigator to obtain a precise estimate of the “true” value.

Data from replicates does present a problem for an investigator because it is rare for data to have such great precision that all of the values for a single type of treatment are exactly the same. This may simply be due to factors that either were not controlled or factors that could not be controlled. So if the values for the data vary (even just a small amount), which one is the “true” value? In a real investigation there is likely no way to know what value is the true value and there is no “right” answer. Instead, the experimental results have to be analyzed statistically. This will provide a method for exactly describing the numerical results. It will also provide a way to compare the results of different treatments to determine if the treatments have any meaningful effect on the reaction.

Another aspect of the post-experiment phase of the investigation is determining the best way to present the results so that conclusions can be reached in an easy and direct manner. The choices involved in the data presentation include:

Using a spreadsheet program like Microsoft Excel can be a valuable tool for the organization and presentation of experimental results. These same programs can also be used for creating graphs of your results.

Use an Excel spreadsheet to organize your raw data from your experiment.

In Excel, columns are named as A, B, C… etc. and rows are named as 1, 2, 3… etc. We can use the same naming convention for referring to a particular cell. Take, for example, a hypothetical set of data for the enzyme reaction run at pH 3. For a set of three replicates of this experiment the data in an Excel spreadsheet might be arranged to look like this:

The absorbance value of Treatment 1-time 0 is in cell B3, which means “B” is the column and “3” is the row.

An alternative arrangement of the same data would be:

Either of these arrangements (or others) can present the data in a table that is easy to use. Once you have the data organized in a spreadsheet, then you can use this arrangement to easily and accurately make calculations based on the data. In the bean experiment from Evolution I, you calculated the total number of alleles and the frequency of the A alleles for each generation using a calculator (or by hand). There are two potential drawbacks to the method you used: it’s laborious and it can result in mistakes. The same information can be generated using the spreadsheet.

Those equations (total number of alleles and frequency of A) could be reentered for every row needed in the spreadsheet. The easier way would be to copy (Ctrl + c) the cells with the formulas and paste (Ctrl + v) them to the remaining cells in the correct place in the spreadsheet. This very quickly allows all the total number of alleles and frequency of A to be calculated correctly for each generation (assuming the correct formulas were used for the first calculations).

For a presentation of the data in the form of a graph from an experiment with three replicates, you could present three separate graphs:

However, that type of presentation makes the direct comparison of the replicates more difficult. An alternative is to place all the replicates on the same graph:

This creates an overly cluttered graph. To get the best graphic representation of the results a statistical averaging of the data will be used. This will be discussed in the next pre-lab section.

Lastly, but perhaps most importantly, how the results of the experiment relate to the original hypotheses and predictions must be addressed. Did your experiment allow you to accept one of your hypotheses and reject the other? Were the predictions you made correct, and if there were problems that occurred that prevented you from reaching a conclusion, can those problems be identified and dealt with if the experiment is to be repeated?

Pre-Lab Quiz

Proceed to the Pre-Lab Quiz