Results 311 to 320 of about 16,679,614 (365)
Some of the next articles are maybe not open access.
Cronbach's alpha reliability: Interval estimation, hypothesis testing, and sample size planning
, 2015Summary Cronbach's alpha is one of the most widely used measures of reliability in the social and organizational sciences. Current practice is to report the sample value of Cronbach's alpha reliability, but a confidence interval for the population ...
D. Bonett, T. A. Wright
semanticscholar +1 more source
2018
In this section, we shall discuss another way to deal with the problem of making a statement about an unknown parameter associated with a probability distribution, based on a random sample. Instead of finding an estimate for the parameter, we shall often find it convenient to hypothesize a value for it and then use the information from the sample to ...
Dharmaraja Selvamuthu, Dipayan Das
openaire +2 more sources
In this section, we shall discuss another way to deal with the problem of making a statement about an unknown parameter associated with a probability distribution, based on a random sample. Instead of finding an estimate for the parameter, we shall often find it convenient to hypothesize a value for it and then use the information from the sample to ...
Dharmaraja Selvamuthu, Dipayan Das
openaire +2 more sources
Hypothesis Testing with Confidence Intervals and P Values in PLS-SEM
Int. J. e Collab., 2016E-collaboration researchers usually employ P values for hypothesis testing, a common practice in a variety of other fields. This is also customary in many methodological contexts, such as analyses of path models with or without latent variables, as well ...
N. Kock
semanticscholar +1 more source
Clinical Nurse Specialist, 1996
Hypothesis testing is the process of making a choice between two conflicting hypotheses. The null hypothesis, H0, is a statistical proposition stating that there is no significant difference between a hypothesized value of a population parameter and its value estimated from a sample drawn from that population. The alternative hypothesis, H1 or Ha, is a
openaire +2 more sources
Hypothesis testing is the process of making a choice between two conflicting hypotheses. The null hypothesis, H0, is a statistical proposition stating that there is no significant difference between a hypothesized value of a population parameter and its value estimated from a sample drawn from that population. The alternative hypothesis, H1 or Ha, is a
openaire +2 more sources
, 1980 The most general situation so far has been testing θ ≤ θ0 against θ> θ0. We next wish to consider testing θ1≤θ≤θ2 against the two-sided alternative θ θ2. We can scarcely hope for a uniformly most powerful test for it would have to compete with the best available tests against the one-sided alternatives θ θ2 taken separately.
openaire +2 more sources
2012
Several key statistical concepts are fundamental not only for hypothesis tests but also for most statistical analyses that arise in clinical studies. Commonly used terms, such as critical values, p-values, and type I and type II errors are defined.
Craig B. Borkowf +2 more
openaire +4 more sources
Several key statistical concepts are fundamental not only for hypothesis tests but also for most statistical analyses that arise in clinical studies. Commonly used terms, such as critical values, p-values, and type I and type II errors are defined.
Craig B. Borkowf +2 more
openaire +4 more sources
Hypothesis testing when a nuisance parameter is present only under the alternative
, 1977SUMMARY We wish to test a simple hypothesis against a family of alternatives indexed by a one-dimensional parameter, 0. We use a test derived from the corresponding family of test statistics appropriate for the case when 0 is given.
R. Davies
semanticscholar +1 more source
On Testing the Utility Hypothesis
The Economic Journal, 1997In order to be able to conduct a test of the (core) utility hypothesis that is not confounded with tests of (subsidiary) hypotheses that economic agents all have the same preferences and that their preferences are weakly separable, it is necessary to use data that are disaggregated and complete.
openaire +4 more sources
The need for Bayesian hypothesis testing in psychological science
, 2017This chapter explains why the logic behind p‐value significance tests is faulty, leading researchers to mistakenly believe that their results are diagnostic when they are not.
E. Wagenmakers +6 more
semanticscholar +1 more source
Hypothesis-testing and t-tests
2002This module covers hypothesis testing using t-tests. Modules 1–3 have covered the preliminary stages in data entry and analysis. Module 2 has provided examples of data exploration and description. Exploring and describing the data using descriptive statistics (means, medians, frequency counts, etc.) and charts provides us with the opportunity to become
Deirdre A. Fullerton +4 more
openaire +2 more sources