### Observed and Expected Ratios:

When two individuals of known genotype are crossed, we expect certain ratios of genotypes and phenotypes in the progeny; these expected ratios are based on the Mendelian principles of segregation, independent assortment, and dominance.

The ratios of genotypes and phenotypes actually observed among the progeny, however, may deviate from these expectations.

For example, in German cockroaches, brown body color (Y) is dominant over yellow body color (y). If we cross a brown, heterozygous cockroach (Yy) with a yellow cockroach (yy), we expect a 1:1 ratio of brown (Yy) and yellow (yy) progeny. Among 40 progeny, we would therefore expect to see 20 brown and 20 yellow offspring. However, the observed numbers might deviate from these expected values; we might in fact see 22 brown and 18 yellow progeny.

Chance plays a critical role in genetic crosses, just as it does in **flipping a coin**. When you flip a coin, you expect a 1:1 ratio— heads and tails. If you flip a coin 1000 times, the proportion of heads and tails obtained would probably be very close to that expected 1:1 ratio. However, if you flip the coin 10 times, the ratio of heads to tails might be quite different from 1:1. You could easily get 6 heads and 4 tails, or 3 and 7 tails, just by chance. It is possible that you might even get 10 heads and 0 tails.

The same thing happens in genetic crosses. We may expect 20 brown and 20 yellow cockroaches, but 22 brown and 18 yellow progeny could arise as a result of chance.

### The Goodness-of-Fit Chi-Square Test

If you expected a 1:1 ratio of brown and yellow cockroaches but the cross produced 22 brown and 18 yellow, you probably wouldn’t be too surprised even though it wasn’t a perfect 1:1 ratio. In this case, it seems reasonable to assume that chance produced the **deviation** between the expected and the observed results. **But, if you observed 25 brown and 15 yellow, would the ratio still be 1:1?** Something other than chance might have caused the deviation.

Perhaps the inheritance of this character is more complicated than was assumed or perhaps some of the yellow progeny died before they were counted.

**Clearly, we need some means of evaluating how likely it is that chance is responsible for the deviation between the observed and the expected numbers**. To evaluate the role of chance in producing deviations between observed and expected values, a **statistical test** called **the goodness-of-fit chi-square test is used**.

This test provides information about how well observed values fit expected values.

Before we learn how to calculate the chi square, it is important to understand what this test does and does not indicate about a genetic cross.

The chi-square test **cannot** tell us whether a genetic cross has been correctly carried out, whether the results are correct, or whether we have chosen the correct genetic explanation for the results.

What it **does indicate** is the **probability** that the **difference** between the observed and the expected values is due to **chance.** In other words, it indicates the **likelihood** that chance alone could produce the deviation between the expected and the observed values. If we expected 20 brown and 20 yellow progeny from a genetic cross, the chi-square test gives the probability that we might observe 25 brown and 15 yellow progeny simply owing to chance deviations from the expected 20:20 ratio.

When the probability calculated from the chi-square test is high, we assume that chance alone produced the difference. When the probability is low, we assume that some factor other than chance—some significant factor—produced the deviation.

**To use the goodness-of-fit chi-square test,**

we first determine the **expected** results. The chi-square test must **always be applied to numbers of progeny, not to proportions or percentages**.

Let’s consider a locus for **coat color in domestic cats**, for which **black color (B) is dominant** over **gray (b).** If we crossed two heterozygous black cats (Bb Bb), we would expect a 3:1 ratio of black and gray kittens. A series of such crosses yields a total of 50 kittens—30 black and 20 gray. These numbers are our observed values. We can obtain the expected numbers by multiplying the expected proportions by the total number of observed progeny. In this case, the expected number of black kittens is 3/4 x 50 = 37.5 and the expected number of gray kittens is 1/4 x 50 = 12.5. The chi-square x^{2} value is calculated by using the following