The probit function Φ(x) is the inverse of the standard normal cumulative distribution function. It maps a value for probability p onto the real number line for values of p between 0 ≤ p ≤ 1. The name “probit” is a contraction of “probability unit” .
The probit function of a probability p gives the z-score associated with p a standard normal distribution. For example, probit(0.5) = 0 and probit(1.96) = .975. In other words, z-values of 0 and 1.96 correspond to the cumulative areas of 0.5(50%) and .975(97.5%) respectively for a standard normal distribution. Essentially, when you find “inv norm” in a statistical package, you’re finding values for the probit function.
The graph is sigmoidal in shape:
Probit Function Use
The probit function was first proposed in 1934 in connection with the percentage of a pest killed by a certain pesticide . Since then, it has found a wide variety of uses in medicine, toxicology, and related fields where risk of exposure to a substance is measured. For example:
- The probit function can be used to describe the relationship between the period of exposure to a toxic chemical, chemical concentrations in air, and response rate .
- The fatality rate of personnel exposed to hazardous agents for a given time period can also be calculated by use of probit functions .
Probit function graph: Geek3,
 Jiang, H. (2021). Machine Learning Fundamentals: A Concise Introduction. Cambridge University Press.
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 Bliss, C. I. (1934). “The method of probits“. Science. 79 (2037): 38–39.
 Bergström, U. et al. (2019). Probit functions for selected chemicals based on AEGL3 values. Retrieved April 13, 2022 from: https://www.foi.se/rest-api/report/FOI-R–4720–SE
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