chi square test statistic

Tuesday, November 18th, 2014

The Chi square compares the observed data with the Null Hypothesis.

Chi square test looks at single set of data and Null Hypothesis.

  • Ho The no difference or no association hypothesis that shows no difference between observed and expected data.
  • H1 This postulates that there is a difference between observed and experimental data.

Expected = row X col total / grand total

χ2 = sum ((observed – expected) /Expected)2

χ2  and DF/degree of freedom gives the test statistic

A big difference between observed and expected results in a large test statistic (χ2) and so leads to a rejection of the Null Hypothesis (Ho)

The greater the value of the test statistic, the greater the evidence against the Null hypothesis -leads to a smaller p -value

“…The p-value is the area under the chi-square probability density function (pdf) curve to the right of the specified χ2 value…” http://www.di-mgt.com.au/chisquare-calculator.h

 

p value area to right

Source: di mgt.com.au

 

p value is the area to the right of the test statistic. The less the number (< 0.05) the more likely to reject the Null Hypothesis

 

 

 

 

 

 

 

http://www.stat.ucla.edu/~kcli/stat13/stat13-lecture14.pdf

 

 

Nice video explaining it all.


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