Inferential Statistics

Definition of Inferential Statistics

Inferential Statistics: Inferential statistics are a type of statistics that are used to make estimations about populations based on samples. This is done by using the data from the sample to calculate a statistic, which is then used to make an inference about the population.

What are Inferential Statistics used for?

Inferential statistics are used to draw conclusions and make inferences based on data. This type of statistical analysis is used to infer the characteristics of a population based on a smaller sample of data. Inferential statistics help to answer questions like: Are two groups significantly different from each other? Does one variable predict another? Is there a statistically significant correlation between variables?

Inferential statistics makes predictions by using methods such as hypothesis testing, chi-square tests, correlation, regression analysis, and more. For example, researchers may use an independent t-test to determine if the average grade in an experimental group differs from another group. Or they may use linear regression analysis to determine whether there is a significant relationship between income level and educational attainment.

In addition to these examples, inferential statistics can also be used for forecasting purposes. This involves analyzing historical data in order to predict future trends or behaviors. For instance, businesses often make decisions about staffing requirements based on past sales trends. By using methods such as time series analysis and ARIMA models, businesses can gain insights from past data and make more accurate predictions about the future.

Overall, inferential statistics are powerful tools that enable us to draw conclusions from limited information and make informed decisions based on our findings.