# LabBench Activity

### Chi-Square Analysis of Data

Look at the tables you printed from Case 1. From the data presented, you can deduce that the F1 cross was between individuals heterozygous for eye color:

+se x +se (+ = red; se = sepia ). From this conclusion, you could write the following hypothesis: "If the parents are heterozygous for eye color, there will be a 3:1 ratio of red eyes to sepia eyes in the offspring." Do your results support this hypothesis?

The actual results of an experiment are unlikely to match the expected results precisely. But how great a variance is significant? One way to decide is to use the chi-square (χ2) test. This analytical tool tests the validity of a null hypothesis, which states that there is no statistically significant difference between the observed results of your experiment and the expected results. When there is little difference between the observed results and the expected results, you obtain a very low chi-square value; your hypothesis is supported.

Next we'll see how to calculate and interpret chi-square. If you would like some help on calculating expected results first, take a side trip before continuing.