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pythonnumpyscipychi-squared
Richard
Richard

Reputation: 65510

Chi squared test in Python

I'd like to run a chi-squared test in Python. I've created code to do this, but I don't know if what I'm doing is right, because the scipy docs are quite sparse.

Background first: I have two groups of users. My null hypothesis is that there is no significant difference in whether people in either group are more likely to use desktop, mobile, or tablet.

These are the observed frequencies in the two groups:

[[u'desktop', 14452], [u'mobile', 4073], [u'tablet', 4287]]
[[u'desktop', 30864], [u'mobile', 11439], [u'tablet', 9887]]

Here is my code using scipy.stats.chi2_contingency:

obs = np.array([[14452, 4073, 4287], [30864, 11439, 9887]])
chi2, p, dof, expected = stats.chi2_contingency(obs)
print p

This gives me a p-value of 2.02258737401e-38, which clearly is significant.

My question is: does this code look valid? In particular, I'm not sure whether I should be using scipy.stats.chi2_contingency or scipy.stats.chisquare, given the data I have.

Upvotes: 15

Views: 37936

Answers (2)

Luca Terzio Pontiggia
Luca Terzio Pontiggia

Reputation: 61

I can't comment too much on the use of the function. However, the issue at hand may be statistical in nature. The very small p-value you are seeing is most likely a result of your data containing large frequencies ( in the order of ten thousand). When sample sizes are too large, any differences will become significant - hence the small p-value. The tests you are using are very sensitive to sample size. See here for more details.

Upvotes: 6

Warren Weckesser
Warren Weckesser

Reputation: 114781

You are using chi2_contingency correctly. If you feel uncertain about the appropriate use of a chi-squared test or how to interpret its result (i.e. your question is about statistical testing rather than coding), consider asking it over at the "CrossValidated" site: https://stats.stackexchange.com/

Upvotes: 5

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