Propensity score matching (PSM) is a commonly used statistical method in orthopedic surgery research that accomplishes the removal of confounding bias from observational cohorts where the benefit of randomization is not possible. An alternative to multiple regression analysis, PSM attempts to reduce the effects of confounders by matching already treated subjects with control subjects who exhibit a similar propensity for treatment based on preexisting covariates that influence treatment selection. It, therefore, establishes a new control group by discarding outlier control subjects. This new control group reduces the unwanted influences of covariates, allowing for proper measurement of the intended variable. An example from orthopedic spine literature is discussed to illustrate how PSM may be applied in practice. PSM is uniquely valuable in its utility and simplicity, but it is limited in that it requires the removal of data and works primarily on binary treatments. In addition to matching, the propensity score can be used for stratification, covariate adjustments, and inverse probability of treatment weighting, but these topics are outside the scope of this paper. Personnel in the orthopedic field would benefit from learning about the function and application of this method given its common use in the orthopedic literature.