## LabBench Activity Genetics of Organisms

### Part of our Genetics Learning Guide

Once data are collected, it is important to be able to determine whether they fall within the range of normal variation for the expected Mendelian ratios. To do this, a chi-square test is used. It will provide a guide as to whether to accept your null hypothesis (which will happen if the observed data fall within the normal variation for the expected ratios) or reject your null hypothesis (if the observed data do not fall within the range of normal variation). The chi-square value gives a measure of the extent to which your results differ from the expected - the larger the chi-square, the bigger the difference, and the less likely that the difference was due to random chance.

### In module 2 we studied molecular genetics

The statistical is that the number of observations in each category is equal to that predicted by a biological theory, and the alternative hypothesis is that the observed numbers are different from the expected. The null hypothesis is usually an extrinsic hypothesis, where you knew the expected proportions before doing the experiment. Examples include a 1:1 sex ratio or a 1:2:1 ratio in a genetic cross. Another example would be looking at an area of shore that had 59% of the area covered in sand, 28% mud and 13% rocks; if you were investigating where seagulls like to stand, your null hypothesis would be that 59% of the seagulls were standing on sand, 28% on mud and 13% on rocks.

In some situations, you have an intrinsic hypothesis. This is a null hypothesis where you calculate the expected proportions after you do the experiment, using some of the information from the data. The best-known example of an intrinsic hypothesis is the Hardy-Weinberg proportions of population genetics: if the frequency of one allele in a population is p and the other allele is q, the null hypothesis is that expected frequencies of the three genotypes are p2, 2pq, and q2. This is an intrinsic hypothesis, because you estimate p and q from the data after you collect the data, you can't predict p and q before the experiment.