hypothesis testing

Hypothesis testing was not performed as part of the present study.

The technique is used for both hypothesis testing and model building.

A different approach to selecting g is through hypothesis testing.

See statistical hypothesis testing for further discussion of this issue.

While hypothesis testing was popularized early in the 20th century, evidence of its use can be found much earlier.

It forms a major component in modern statistical hypothesis testing.

They argue for the development of theoretical models, upon which hypothesis testing can be carried out.

This parallels the reliance on positive tests in hypothesis testing.

This is a useful property of indicator variables, especially for hypothesis testing.

In this case a single multivariate test is preferable for hypothesis testing.

An introductory college statistics class places much emphasis on hypothesis testing - perhaps half of the course.

A standard approach for solving this kind of problem is multiple hypothesis testing (Singer et al. 1974).

Hypothesis testing is the crucial element of scientific thought.

Statistical hypothesis testing is considered a mature area within statistics, but a limited amount of development continues.

Hypothesis testing has been taught as received unified method.

The scientific method is about hypothesis testing and experimentation.

Applying a statistical test of significance (hypothesis testing) to the same data the pattern was learned from is wrong.

Hypothesis testing is also taught at the postgraduate level.

That is the proliferation of formal-deductive model building and quantitative hypothesis testing.

Other forms of observation-based hypothesis testing are not considered to be "empirics."

Skewness risk plays an important role in hypothesis testing.

The analysis of variance, the most common test used in hypothesis testing, assumes that the data is normally distributed.

Statistical hypothesis testing involves performing the same experiment on multiple subjects.

This probability distribution has applications to hypothesis testing and multiple comparisons.

Neyman & Pearson considered a different problem (which they called "hypothesis testing").