Warm-Up: Chi Square Practice!!!

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Presentation transcript:

Warm-Up: Chi Square Practice!!! When did we do this before? Why would we be doing it now? (Hint: think about our next research lab) What are our groups? What ratio do we expect? With a partner, work together to fill out the example on slides 6 and 7. Each pair should turn in a copy to Mr. Koons.

Chi Square: Goodness of Fit Test The Chi Square Goodness of Fit Test determines if a set of data is significantly different from an expected outcome. Calculates a chi square value! Our Null says that the IV has NO effect on the DV If a Chi Square Test indicates that you should reject the Null, then there IS a significant difference between the expected values and the experimental group values.

Chi Square: Goodness of Fit Test The Chi Square Table:

Chi Square: Goodness of Fit Test Degrees of Freedom (df) = # of groups minus one

Chi Square: Goodness of Fit Test Degrees of Freedom (df) = # of groups minus one If the calculated chi-square value is greater than or equal to this critical value, then the two groups ARE significantly different, and the null hypothesis is rejected. If the null hypothesis is rejected and we are 95% confident that there is significant difference. If the calculated chi-square value is less than this critical value, then the two groups are NOT significantly different, and the null hypothesis is not rejected/accepted.

Chi Square: Goodness of Fit Test Practice: Let’s look at the number of males and females in our class. Biologically, there is a 50/50 chance of a couple having a boy or a girl. Therefore, our expected number of males and females in a class of 40 students is . Count the number of boys and girls in our class. Does this significantly differ from the expected values? Extra credit if you can make the appropriate connection to the p value!

Chi Square: Goodness of Fit Test The Chi Square Table:

If Chi Square value is greater than or equal to critical value, reject the Null (is significantly different). If Chi Square value is less than the critical value, do not reject the Null (not significantly different).