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Published byEvan Sidwell Modified about 1 year ago

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Chi-Square Test Chi-square is a statistical test commonly used to compare observed data with data we would expect to obtain according to a specific hypothesis. For example, if, according to Mendel's laws, you expected 10 of 20 offspring from a cross to be male and the actual observed number was 8 males, then you might want to know about the "goodness to fit" between the observed and expected. Were the deviations (differences between observed and expected) the result of chance, or were they due to other factors. – How much deviation can occur before you, the investigator, must conclude that something other than chance is at work, causing the observed to differ from the expected.

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How does it work? The equation: X 2 = Your probability value = the sum of O= your observed data e= your expected data

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How does it work? Determine your null hypothesis. – This the hypothesis that states that there will be no significant difference between the data. What would the null hypothesis be for our experiment?

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How does it work? You first need to determine what your expected values would be. In this case, we are assuming that 90% of the cells counted should be in Interphase. So, determine how many cells you would expect to see in interphase. For the remaining phases, we will say that they should spend an equal amount of time in each. Determine how many cells should be in Prophase, Metaphase, Anaphase and Telophase.

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How does it work? Once you have your expected values, you can begin your test. – Input your observed and expected values into the chi square formula. – We will first do one for each category individually. This will show us if there was any statistical difference within the categories. Interphase Prophase Metaphase Anaphase Telophase

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How does it work? We can now do a chi-square test for the entire data set that was found in the lab. – Use the chi-square formula to add up all of the data.

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Now we can use the data… Once you have the chi-square values, we need to determine two things: – Degrees of freedom (number of categories minus 1 1 can be the smallest degree of freedom) – The chi-square table value (if the chi-square value is larger than the table value, then there is a significant statistical difference) Alternatively: if your calculated chi-square value is less than the table value, than the difference is due to chance.

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What does it all mean? Was there a statistical difference between the values? What does this tell us about the data?

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