7 Types of Statistical Analysis Techniques

Related: Definitive Guide To Understanding Descriptive Statistics
Inferential statistical analysis
Inferential statistical analysis is used to make inferences or draw conclusions about a larger population-based on findings from a sample group within it. It can help researchers find distinctions among groups present within a sample. Inferential statistics are also used to validate generalizations made about a population from a sample due to its ability to account for errors in conclusions made about a segment of a larger group.
To perform inferential statistical analysis, researchers estimate the parameters of the population from the sample. They can also perform a test of statistical hypothesis to arrive at a confidence interval that validates or disproves the generalizations made from the sample.
Associational statistical analysis
Associational statistics is a tool researchers use to make predictions and find causation. They use it to find relationships among multiple variables. It is also used to determine whether researchers can make inferences and predictions about a data set from the characteristics of another set of data. Associational statistics is the most advanced type of statistical analysis and requires sophisticated software tools for performing high-level mathematical calculations. To measure association, researchers use a wide range of coefficients of variation, including correlation and regression analysis.
Other types of statistical analysis
Below are four other types of statistical analysis:
Predictive analysis
Predictive analysis uses powerful statistical algorithms and machine learning tools to predict future events and behavior based on new and historical data trends. It relies on a wide range of probabilistic techniques such as data mining, big data, predictive modeling, artificial intelligence and simulations to guess what is likely to occur in the future.
Predictive analysis is a branch of business intelligence as many organizations with operations in marketing, sales, insurance and financial services rely on data to make long-term plans. It is important to note that predictive analysis can only make hypothetical forecasts and the quality of the predictions depends on the accuracy of the underlying data sets.