This review discusses the definition, estimation, and interpretation of Wright's F-statistics, particularly F_ST, in geographically structured populations. F_ST is a key descriptive statistic in population and evolutionary genetics, providing insights into genetic differentiation among populations. It is widely used in fields such as disease association mapping and forensic science. The review explains how F_ST is defined, how it should be estimated, and how it relates to other statistics. It also discusses the importance of F_ST in understanding population structure and evolutionary dynamics. The review highlights that genetic differentiation among populations is influenced by factors such as genetic drift and divergent natural selection. Recent studies suggest that only 5–10% of human genetic diversity is explained by differences among populations from major geographical regions. This indicates that there are more similarities than differences among geographically distinct human populations. The review explains that F_ST is related to the variance in allele frequency among populations and the degree of resemblance among individuals within populations. It also discusses how F_ST can be used to identify regions of the genome that have been subjected to diversifying selection. Additionally, the review discusses other statistics related to F_ST, such as G_ST, R_ST, Φ_ST, and Q_ST, and clarifies their differences and appropriate uses. The review also addresses the challenges of estimating F_ST, including statistical sampling and genetic sampling. It discusses different methods for estimating F_ST, such as the method of moments, maximum likelihood, and Bayesian methods. The review emphasizes the importance of considering these methods when interpreting F_ST estimates. The review concludes that F_ST is a crucial tool in population and evolutionary genetics, providing insights into the genetic structure and evolutionary dynamics of populations. It also highlights the importance of understanding the limitations and assumptions of F_ST estimates in different contexts. The review underscores the significance of F_ST in understanding genetic variation and its applications in various fields.This review discusses the definition, estimation, and interpretation of Wright's F-statistics, particularly F_ST, in geographically structured populations. F_ST is a key descriptive statistic in population and evolutionary genetics, providing insights into genetic differentiation among populations. It is widely used in fields such as disease association mapping and forensic science. The review explains how F_ST is defined, how it should be estimated, and how it relates to other statistics. It also discusses the importance of F_ST in understanding population structure and evolutionary dynamics. The review highlights that genetic differentiation among populations is influenced by factors such as genetic drift and divergent natural selection. Recent studies suggest that only 5–10% of human genetic diversity is explained by differences among populations from major geographical regions. This indicates that there are more similarities than differences among geographically distinct human populations. The review explains that F_ST is related to the variance in allele frequency among populations and the degree of resemblance among individuals within populations. It also discusses how F_ST can be used to identify regions of the genome that have been subjected to diversifying selection. Additionally, the review discusses other statistics related to F_ST, such as G_ST, R_ST, Φ_ST, and Q_ST, and clarifies their differences and appropriate uses. The review also addresses the challenges of estimating F_ST, including statistical sampling and genetic sampling. It discusses different methods for estimating F_ST, such as the method of moments, maximum likelihood, and Bayesian methods. The review emphasizes the importance of considering these methods when interpreting F_ST estimates. The review concludes that F_ST is a crucial tool in population and evolutionary genetics, providing insights into the genetic structure and evolutionary dynamics of populations. It also highlights the importance of understanding the limitations and assumptions of F_ST estimates in different contexts. The review underscores the significance of F_ST in understanding genetic variation and its applications in various fields.