Stratified Random Sampling

What Does Stratified Random Sampling Mean?

Stratified random sampling is a data analysis method that involves dividing a population into distinct groups or strata and then randomly selecting samples from each group. The sample size from each stratum is proportional to its size relative to the overall population. This approach ensures a more representative sample for the variable being studied. Insurance companies commonly use this technique to estimate expected losses more accurately, helping them set appropriate premiums for their policies. It’s one of the statistical methods behind how car insurance is calculated, since insurers need accurate data on specific subgroups of drivers to price policies fairly.

Insuranceopedia Explains Stratified Random Sampling

For instance, if an automobile insurance company wants to determine the average number of car accidents involving male high school seniors in Maryland, it could use stratified random sampling. By dividing the population into strata based on different high schools across the state and then selecting random samples from each stratum, the company can obtain a more accurate estimate. This approach allows for setting premiums that more accurately reflect the associated risk. Data collected this way feeds into the broader set of factors that affect car insurance rates, including age, driving history, location, and vehicle type. The Maryland example also explains why car insurance for teens in Maryland tends to cost more than policies for older drivers, since sampled accident data for younger drivers points to higher expected losses.