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Pearson's Goodness of Fit Test is used to compare the observed distribution of a categorical variable to some historical reference.

Let \(X_i\) be the response from the \(i^{th}\) observation taking values in \( \{x_1, \dots, x_r \} \). If we let \(p_k = P(X_i = p_k) \) for \( k = 1, \dots, r \), Pearson's goodness of fit test tests the hypothesis:

\[ H_0: p_k = p_{k, 0} \text{ versus } H_a: p_k \ne p_{k,0} \]where \(p_{k,0}\) is some reference value.

To input multiple values, seperate them by a comma.

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- Solve For
- The unknown you are interested in solving for.
- N
- The sample size used to test the hypothesis.
- Alpha
- The \(\alpha\) (Type I error rate) level of the hypothesis test.
- Power
- The power (1 - Type II error rate) of the hypothesis test.
- \(p_0\)
- A comma seperated list of the reference probabilities.
- p
- A comma seperated list of the observed probabilities.

**Note:** \(p\) and \(p_0\) must have the same number of probabilites.

No calculation has been generated yet.

N | Alpha | Power |
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- Chow, S., Shao, J., & Wang, H. (2003),
*Sample size calculations in clinical research,*New York: Marcel Dekker.