Senior mathematics student Kimberly Hildebrand’s abstract titled ** Using Independent Bernoulli Random Variables to Model Gender Hiring Practices **has been accepted for presentation at the 2014 National Pi Mu Epsilon Conference from August 6 to 8 in Portland, Ore.

Here is the abstract:

Gender bias is a problem in the workforce at large. In order for society to progress it is important that hiring practices do not use gender as a competitive factor. Hiring practices based on gender can be represented statistically using Bernoulli Random Variables and the Beta and Binomial Distributions. Using the moment generating function (MGF) of the Bernoulli and Binomial Distributions, it is possible to calculate the expected value (mean) and variance for the number of women hires for n positions. The probability generating function (PGF) of a sample size n can be used to find the probability of hiring a specific number of women (X). A computer program was used to run trials to simulate different male/female distributions using recent data on the proportion of women earning a PhD in a variety of disciplines. The simulations were used to represent hiring results for seven faculty positions. Situations where the female proportion is centered at 0.3, 0.5, and 0.7 were studied. Trials that included random proportions of women for each position were run as well. Results revealed that it is actually unusual for employers to hire one or fewer women for seven positions, which could provide evidence of gender bias.