What is Inferential Analysis

The next type of data science analysis method is inferential analysis. The purpose of inferential analysis is to use a small sample of data to generalize something about a bigger population. Inferential analysis is commonly the goal of statistical modelling, where you have a small amount of information to extrapolate and generalize that information to a larger group.Inferential analysis typically involves using the data you have to estimate that value in the population and then give a measure of your uncertainty about your estimate. Since you are moving from a small amount of data and trying to generalize to a larger population, your ability to accurately infer information about the larger population depends heavily on your sampling scheme, if the data you collect is not from a representative sample of the population, the generalizations you infer won’t be accurate for the population. Unlike in our previous examples, we shouldn’t be using census data in inferential analysis - a census already collects information on (functionally) the entire population, there is nobody left to infer to; and inferring data from the US census to another country would not be a good idea because the US isn’t necessarily representative of another country that we are trying to infer knowledge about. Instead, a better example of inferential analysis is a study in which a subset of the US population was assayed for their life expectancy given the level of air pollution they experienced. This study uses the data they collected from a sample of the US population to infer how air pollution might be impacting life expectancy in the entire US.