Χ 2 (Chi-square) Test
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Transcript of Χ 2 (Chi-square) Test
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
What is a χ2 (Chi-square) test used for?
Statistical test used to compare observed datawith expected data according to a hypothesis.
Let’s look at the next slide to find out…
What does that mean?
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
Ex. Say you have a coin and you want to determine if it is fair (50/50 chance of gets heads/tails). You decide to flip the coin 100 times. If the coin is fair what do you expect/predict to observe?
50 heads and 50 tails
Now come up with a hypothesis (two possibilities)
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
Hypotheses1. The coin is fair and there will be no real difference between what we will observe and what we expect.
2. The coin is not fair and the observed results will be significantly different from the expected results.
The first hypothesis that states no difference between the observed and expected has a special name… NULL HYPOTHESIS
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
NULL HYPOTHESISThis is the hypothesis that states there will be no difference between the observed and the expected data or that there is no difference between the two groups you are observing.
Ex. You wonder if world class musicians have quicker reaction times than world class athletes. What would the null hypothesis be?
That there is no difference between these two groups.
Let’s get back to flipping coins…
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
You flip the coin 100 times and you getting the following results:
Heads TailsObserved 41 59
50 50Expected
Is the coin fair or not?
It’s not easy to say. It looks like it might, but maybe not…This is where statistics, in particular the χ2 test, comes in.
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
The formula for calculating χ2 is:
Where O is the observed value and E is the expected.
What happens to the value of χ2 as your observed data gets closer to the expected?
Χ2 approaches 0
Let’s determine χ2 for the coin flipping study…
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
Heads TailsObserved 41 59
50 50Expected
Χ2 = (41-50)2/50 + (59-50)2/50
Χ2 = (-9)2/50 + (9)2/50
Χ2 = 81/50 + 81/50
Χ2 = 3.24
So what does this number mean…?
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
Statisticians have devised a table to do this:
Converting Χ2 to a P(probability)-value
Great, but how do you use this?
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
Converting Χ2 to a P(probability)-value
First we need to determine Degrees of Freedom (DoF):
DoF = # of groups minus 1
We have two groups, heads group and tails group. Therefore our DoF = 1.
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
Converting Χ2 to a P(probability)-value
Then scan across and find your X2 value (3.24)
P-value = ~0.07Lastly go up and estimate the p-value…
What does this value tell us?
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
The P-value
The p-value tells us the probability that the NULL hypothesis (observed and expected not different) is correct.
P-value = ~0.07
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
Heads TailsObserved 41 59
50 50Expected
P-value = ~0.07
Therefore, there is a 7% chance that the null hypothesis (there is no real difference between observed and expected) is correct.
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
Heads TailsObserved 41 59
50 50Expected
P-value = ~0.07
You might say then that the other hypothesis must be correct as there is a 93% likelihood that there is a different between observed and expected…
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
50 50
P-value = ~0.07
However, statisticians have a p-value = 0.05 cutoff. In order for the hypothesis to be supported, p must be less than 0.05 (5% chance that null is correct).
Therefore the null hypothesis cannot be rejected.
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
For example, suppose that a cross between two pea plants yields a population of 880 plants, 639 with green seeds and 241 with yellow seeds. You are asked to propose the genotypes of the parents. Your hypothesis is that the allele for green is dominant to the allele for yellow and that the parent plants were both heterozygous for this trait. If your hypothesis is true, then the predicted ratio of offspring from this cross would be 3:1 (based on Mendel's laws) as predicted from the results of the Punnett square. Is it?
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve
Χ2 (Chi-square) TestChapter 13 – How Populations Evolve