1-cdf value vs chi-squared statistic mislabeled on site?

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SUMMARY

The discussion centers on the labeling of a graph in the Wikipedia article on Pearson's chi-squared test, which is incorrectly identified as the "chi-squared distribution." The vertical axis represents 1 minus the chi-squared cumulative probability distribution values, not the probability density function (PDF). The confusion arises from the graph's labeling, which misleads users into thinking it depicts the PDF rather than the cumulative distribution function (CDF). The clarification provided indicates that the graph is a function of a statistic, not a random variable, resolving the initial confusion.

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nomadreid
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I am confused by the labeling of the (only) graph in
http://en.wikipedia.org/wiki/Pearson's_chi-squared_test
The values on the vertical axis appear to be the values of 1-(the chi-squared cumulative probability distribution values*)
as I would expect in finding the p-values.
However, this graph is labeled the "chi-squared distribution" -- which is ambiguous enough, but when one clicks on this term under the graph, one is directed to a site where the term means the chi-squared probability density function. But the graph cannot be the probability density function: the values are different*, and a probability density function has probability densities on the vertical axis, not probabilities.
So, is this mislabeled?

(*The pdf and cdf graphs were taken from http://en.wikipedia.org/wiki/Chi-squared_distribution#Probability_density_function, and individual values, for more precision, from http://www.danielsoper.com/statcalc3/calc.aspx?id=11)
 
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The graph isn't actually labeled as the chi-squared distribution. It's merely saying it's a variation of the chi-squared distribution. The density function given on the second page is correct. Chi-squared is just a gamma distribution so it's obvious the first page doesn't show a standard chi-squared. The graph on the first page is just the function of a statistic, not a random variable.
 
ahhhhh. That solves that mystery. Thanks, jwatts.
 

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