You may have used probability in your genetics class to construct a Punnet square. You most likely have already been exposed to the topic of probability in earlier courses. In laboratory quality control, where it is common to make only two or three measurements, what is the probability of detecting medically important errors when using a Levey-Jennings control chart with control limits set as the mean plus and minus 2 SD? What is the chance of a false rejection, i.e., an out-of-control signal even though the method is working properly? For example, in evaluating the performance of a new laboratory method by analyzing 40 specimens on a new method and a comparison method, what is the probability, or chance, that the difference observed between methods represents a real measurement error and not just a figment of the noise in the data (random error, imprecision, or scatter). Data is often limited because of cost considerations, therefore, it is very important to interpret that data properly.

Probability is important because it helps us understand the chances of making a correct inference or decision on the basis of a limited amount of data. In fact, the road may be quite smooth once you understand probability and its usefulness for interpreting measurement data.

Our objective in this lesson is to get by that roadblock and demonstrate that what lies beyond is not really so bad. Probability is one of those statistical terms that may cause a mental roadblock for students.

Table of areas under a standard normal curve.

EdD Assistant ProfessorĬlinical Laboratory Science Program University of Louisville And how that bell-shaped curve came to be. How z-scores can help us find probabilities. How a coin toss relates to laboratory testing.

Standard normal curve z score table 30