Chi Square

Today we are going to be learning about Chi Square (Yes there is a little bit of math, do not freak out)

Chi is not pronounced like Chai Tea but Chi always makes me think of Chai...

So to ease us into learning about Chi Square Tests we are first going to make ourselves Chai Tea. This way we can enjoy our Chai while we learn about Chi.

Lecture:

Chi Square tests are very important in the scientific community. Once data is collected from an experiment it is up to the researcher to prove whether or not the results are "significant." Science tries to stay objective which means researchers should avoid inserting their opinion into their data interpretation.

For example if you were to conduct an experiment where 75% of your test subjects performed better on a test when given an experimental medication you could easily say that the medication causes a significant improvement in test performance but that would be your opinion that 75% is significant. There are other variables involved for example was the study group only 4 people? If so than that means the medication only helped 3 people. That might not be enough to be significant.

A Chi Square test will show if experiment data is significant or not without the opinion of the researcher.

From here on out any lab that you perform in AP Bio will require a Chi Square test to show whether or not your data was significant.

Here is the formula for a Chi Squared Test

The O stands for Observed Data and the E stands for expected data

When we do any type of experiment we need to first create a Null Hypothesis. The Null Hypothesis is the opposite of your hypothesis.

Your Null Hypothesis is almost always:

"There is no significant difference between the observed and expected frequencies."

The whole point of a Chi Square is to accept or reject your Null Hypothesis

Now lets take a look at the Chi Square Chart

We first need to figure out our degrees of freedom. The degrees of freedom for your experiment are your number of outcomes minus 1. So if we have two possible outcomes then our degrees of freedom is 1. We cannot do a Chi Square Test for an experiment that only has one outcome because there is nothing to compare it to.

Second we need to find our critical value

For biology we almost always use a critical value of 0.05 (This means that we are 95% sure of our data)

If your Chi Squared Value is higher than your critical value then you reject your Null hypothesis (which means the data supports your true hypothesis.) If the value is under your critical value then you accept your null hypothesis.

Student Group Practice:

Now lets try a simple Chi Square Test on flipping a coin

Get into your groups. You are going to hypothesize that when flipping a coin one side of the coin will face up more times than the other.

This means that your null hypothesis will be "When flipping a coin there will be no difference in the amount of times heads shows up compared to tails"

Now lets run the experiment. Flip a coin 30 times (The more data you get the more accurate your study data will be)

Now lets take our outcomes data and plug that into our Chi Square Formula

Student Group Work:

Now lets work on our animal Behavior Lab reports