Probability Models

Probabilities are numbers that reflect the likelihood that a particular event or outcome will occur. In biostatistical applications, it is probability theory that underlies statistical inference. Statistical inference involves making generalizations or inferences about unknown population parameters. After selecting a sample from the population of interest, we measure the characteristic under study, summarize this characteristic in our sample and then make inferences about the population based on what we observe in the sample.

In this course, we discuss probability models, and two in particular – the binomial distribution model and the normal distribution model.  

After completing this course, learners will be able to:

• Explain the key features of the binomial distribution model
• Calculate probabilities using the binomial formula
• Explain the key features of the normal distribution model
• Calculate probabilities using the standard normal distribution table
• Compute and interpret percentiles of the normal distribution
• Define and interpret the standard error
• Apply and interpret the results of the Central Limit Theorem

Developed by: Lisa Sullivan, PhD; Wayne LaMorte, MD, PhD, MPH