Search results
Results from the WOW.Com Content Network
Statistical inference makes propositions about a population, using data drawn from the population with some form of sampling.Given a hypothesis about a population, for which we wish to draw inferences, statistical inference consists of (first) selecting a statistical model of the process that generates the data and (second) deducing propositions from the model.
Different statistical foundations may provide different, contrasting perspectives on the analysis and interpretation of data, and some of these contrasts have been subject to centuries of debate. [2] Examples include the Bayesian inference versus frequentist inference ; the distinction between Fisher 's significance testing and the Neyman ...
Statistical hypothesis testing is a key technique of both frequentist inference and Bayesian inference, although the two types of inference have notable differences. Statistical hypothesis tests define a procedure that controls (fixes) the probability of incorrectly deciding that a default position ( null hypothesis ) is incorrect.
While the tools of data analysis work best on data from randomized studies, they are also applied to other kinds of data—like natural experiments and observational studies [19] —for which a statistician would use a modified, more structured estimation method (e.g., difference in differences estimation and instrumental variables, among many ...
The difference between these assumptions is critical for interpreting a hypothesis test. There are broadly two camps of statistical inference, the epistemic approach and the epidemiological approach. The epistemic approach is the study of variability; namely, how often do we expect a statistic to deviate from some observed value.
Additionally, the term 'inference' has also been applied to the process of generating predictions from trained neural networks. In this context, an 'inference engine' refers to the system or hardware performing these operations. This type of inference is widely used in applications ranging from image recognition to natural language processing.
The differences between these interpretations are rather small, and may seem inconsequential. One of the main points of disagreement lies in the relation between probability and belief. Logical probabilities are conceived (for example in Keynes ' Treatise on Probability [ 12 ] ) to be objective, logical relations between propositions (or ...
In statistics education, informal inferential reasoning (also called informal inference) refers to the process of making a generalization based on data (samples) about a wider universe (population/process) while taking into account uncertainty without using the formal statistical procedure or methods (e.g. P-values, t-test, hypothesis testing, significance test).