When testing mathematically through chi-square, null and alternative hypotheses are used to frame the statistical test.
You will have the option to learn chi-square (optional edpuzzle) if you are confident in math and would like to 'earn points' in that section, but it recently has not been showing up in detail on the exam. If you are not confident in math, your best bet is to spend your time reviewing the biology so you can earn the points there.
A null hypothesis is a prediction that can be rejected if the data shows changes from 'zero effect' of the independent variable on the dependent variable. For instance, if I tried a new brand of fertilizer on grass, my null hypothesis could be "Application of Super-Gro does not affect the growth of grass." If I saw that super-gro treated grass grew less (and did a chi square test to show that it was statistically significant), that null hypothesis would be rejected. If there was no statistically significant difference in Super-gro treated grass vs non-treated grass, then the null hypothesis would be supported.
An alternative hypothesis is a prediction that is more directional. It's what we think of as a traditional hypothesis. "Application of super-gro increases the growth of grass". Of course, that alternative hypothesis leaves space for two outcomes: either super-gro has no effect, or it causes a decrease in grass growth. So you could have a second alternative hypothesis; "Application of super-gro decreases the growth of grass."
Notation uses subscripts:
H0 = Application of Super-Gro does not affect the growth of grass.
HA1 = Application of Super-Gro increases the growth of grass.
HA2=Application of Super-Gro increases the growth of grass.
Using both the null and alternative hypothesis during statistical testing lets you clearly determine what happened during the experiment, and whether the experiment's data supports or rejects each hypothesis.
The likely thing that you will be asked to do, rather than calculate all of chi-square, is to write or identify a null or alternative hypothesis, or determine whether those hypotheses are supported or rejected based on the data.
You could also be asked to interpret a chi-square number, which doesn't require doing all the math.