Do We Really Need Zero-Inflated Models?

Do We Really Need Zero-Inflated Models?
Source: Statistical Horizon blog by Paul Allison
 
"... Of course, there are certainly situations where a zero-inflated model makes sense from the point of view of theory or common sense. For example, if the dependent variable is number of children ever born to a sample of 50-year-old women, it is reasonable to suppose that some women are biologically sterile. For these women, no variation on the predictor variables (whatever they might be) could change the expected number of children.

So next time you’re thinking about fitting a zero-inflated regression model, first consider whether a conventional negative binomial model might be good enough. Having a lot of zeros doesn’t necessarily mean that you need a zero-inflated model."

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This question has haunted me for a while, thank Dr. Allison answered this question in such a layman-kind way. I like his book "Survival Analysis Using SAS: A Practical Guide" much; I don't have his book "Logistic Regression Using SAS: Theory and Application". Hope this logistic regression related book is in the same style.

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