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Published byXavier Flood Modified over 5 years ago

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**What is Chi Square? Chi square is a “goodness of fit” test.**

It is also known as (aka) x2 It is a statistical test to tell whether differences between observed (count from sample) and our expected (the claim) are due to chance or some other reason. (foul play perhaps?)

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How to use Chi Square? First you should form a hypothesis ex. The company is not doing their job correctly and the actual ratio of colors is different from their claim. Second you should form a null hypothesis (opposite of your hypothesis)ex. If the company is correct, there should be no difference between the actual ratio of colors and the percentages they say. Third you should have your expected and observed numbers ex. 15% for yellows actual and 20% observed

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How to use Chi Square? Fourth you should calculate a chi square value for each set of results ex. X2= (15-20)2 =

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How to use Chi Square? Fifth add the chi square values together ex. your percentages from yellow and green and blue, etc. Sixth determine your degrees of freedom ex. However many options or colors you have minus 1

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How to use Chi Square? Seventh, look up your chi square value in the appropriate row for your degrees of freedom If your number falls below the .05 (5%) value then you can accept the null hypothesis, if it is above then it must be rejected

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How to use Chi Square? Lastly, you can accept or reject your null hypothesis. Remember: If the company is correct, there should be no difference between the actual ratio of colors and the percentages they say.

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What does it mean? Significance cut-offs- at p=0.05, There is a 5% chance that another trial will give you a worst fit than what you have. The higher the percentage the higher the chance of a worst fit because it is already so close to expected. The lower the percentage, the lower chance of a worst fit because you are already so far off from expected. 5% is the significance cut-off scientists accept as minimum.

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What does it mean? Worst Fit- getting results that are further off from expected than your first results. The greater the possibility (p value) for a worst fit, the better or more acceptable your results are. The lower the percentage (p value), the greater chance of a worst fit, the more rejectable your results are.

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**Let’s get to it! Click back to the home page and click on the lab!**

What does it mean? Got it? No? Think of it this way, the higher your chi-square value, the lower your p-value, your null hypothesis is rejected. The lower your chi-square value, the higher your p-value, your null hypothesis is accepted. Okay? Let’s get to it! Click back to the home page and click on the lab!

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