# Wald, Score, Likelihood ratio

Three different ways are used to make large sample inferences all using a X^{2} statistic, so it is important to understand the differences to avoid confusion.

The score, likelihood ratio, and Wald evidence functions are useful when analyzing categorical data. All have approximately chi-squared (X^{2}) distribution when the sample size is sufficiently large.

Because of their underlying use of the X^{2} distribution, mixing of the techniques often occurs when testing hypotheses and constructing interval estimates. Introductory textbooks often use a score evidence function for the hypothesis test then use a Wald evidence function for the confidence interval. When you use different evidence functions the results can be inconsistent. The hypothesis test may be statistically significant, but the confidence interval may include the
hypothesized value suggesting the result is not significant. Where possible you should use the same underlying evidence function to form the confidence interval and test the hypotheses.

Note that when the degrees of freedom is 1 the Pearson X^{2} statistic is equivalent to a squared score Z statistic because they are both based on the score evidence function. A 2-tailed score Z test produces the same p-value as a Pearson X^{2} test.

Many Wald-based interval estimates have poor coverage and defy conventional wisdom. Modern computing power allows the use of computationally intensive score evidence functions for interval estimates, such that we recommended them for general use.

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- What is Analyse-it?
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- Distribution
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- Contingency tables
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- Estimators
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- Relative risk
- Inferences about equality of proportions
- Equality of proportions hypothesis test
- Exact and asymptotic p-values
- Wald, Score, Likelihood ratio
- Tests for equality of proportions (independent samples)
- Testing equality of proportions (independent samples)
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- Mosaic plot
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Version 5.65

Published 14-Aug-2020