Statistics that let you draw probable conclusions beyond your immediate universe of data are called:

The concept of inferential statistics is essential in research and analysis as it allows researchers to make conclusions or inferences about a larger population based on a sample of data. This branch of statistics employs various techniques to estimate population parameters, test hypotheses, and predict trends, enabling the extension of findings beyond the immediate data set being examined.

Inferential statistics often involves the use of probability theory to account for the uncertainty inherent in drawing conclusions about a population from a sample. By utilizing methods such as confidence intervals, hypothesis tests, and regression analysis, researchers can formulate insights that reflect broader trends and characteristics, making it a key aspect of statistical analysis in various fields, including health and nutrition.

Descriptive statistics, on the other hand, focus on summarizing and organizing the data collected from a specific sample without making broader generalizations. This includes measures like mean, median, mode, and standard deviation, which provide a picture of the existing data but do not allow for conclusions beyond that data.

Variable interest statistics and continuous statistics are not standard terms used in statistical methodology. The former implies a focus on specific variables, which could be part of inferential or descriptive statistics but does not indicate a systematic statistical approach. Continuous statistics might refer to data types that can take any value within