Sentences with phrase «significance tests»

An interactive web site is available that conducts the Sobel test (with significance tests) if path coefficients and standard errors are entered (http://quantrm2.psy.ohio-state.edu/kris/sobel/sobel.htm).

For 2 - group comparisons that survived the Bonferroni correction, we proceeded to significance tests on the individual variables, including combined and separate youth - and parent - reported measures.

Topics include (1) elements of the research process; (2) types of designs, program evaluation; (3) ethical considerations of research: informed consent, research with diverse and vulnerable populations, research with children, human subjects review; (4) basic measurement concepts: validity, reliability, norms, score interpretation; and (5) basic statistical concepts: frequency distributions, central tendency, measures of variability, correlation, normal curve, hypothesis testing, significance tests.

In these procedures, significance tests of indirect effects used the bootstrap generated confidence interval (CI) estimations recommended by Hayes (2012) and maintained the conventional focus on unstandardized regression coefficients.

Owing to the high number of significance tests, the significance level was set to 1 % (Wald test).

Improved clients» email campaign performance with effectively adopting GLM models and significance tests in SAS on the campaign offers and audience clusters;

The story didn't indicate whether any significance tests were done on the data, and without the exact baseline on the % of reversals of the lower court for these cases, I couldn't do the statistics myself.

The GISS temperature series does not meet the theoretical requirements that would allow you to make the significance tests you are quoting.

Statistical significance tests?

Update 22 Aug 2010: Additional significance tests that we have performed indicate that the NH land + ocean Had reconstruction with all tree - ring data and 7 potential «problem» proxies removed (see original Supp Info where this reconstruction is shown) yields a reconstruction that passes RE at just below the 95 % level (approximately 94 % level) back to AD 1300 and the 90 % level back to AD 1100 (they pass CE at similar respective levels).

Whether or not these trends are statistically significant, and whether they hold up the longer run, and whether they are caused by CO2 or whatever are all interesting issues that require significance tests and confidence intervals.

There are some significance tests that are incorrectly implemented (white noise / Gaussian assumption, no DOF justification).

... would only demonstrate what happens when you drastically change the degrees of freedom, alterations in the spread and skewness of the data, and perform faulty significance tests incorrect assumptions about normal distribution of data.

When I give diagnostics, like trends and so on, I use standard mathematical tools and significance tests.

I just work at the level of basic significance tests for regression lines and so on, which are okay as a ball park starting point but not really up to a proper hypothesis test.

A reader recently asked my opinion about this post at Skeptical Science, which is a comment on Ambaum 2010, Significance Tests in Climate Science, J. Climate, doi: 10.1175 / 2010JCLI3746.1.

Look at all the significance tests and tests of the null hypothesis done by climate scientists.

An examination of these connections suggest that recent concerns over the lack of reproducibility of scientific studies can be attributed largely to the conduct of significance tests at unjustifiably high levels of significance.

The onus on anyone seeking to carry out significance tests using an RE statistic in an MBH98 context has to either start with a 99 % benchmark or prove that a lower benchmark can be used.

Below, I've shown the detail behind the summary above, all using data and significance tests from he NAEP Data Explorer.

Statistical significance tests.

... It is a safe bet that people have suffered or died because scientists (and editors, regulators, journalists and others) have used significance tests to interpret results, and have consequently failed to identify the most beneficial courses of action.»

Statistical significance tests were based upon logistic regression20 and a series of binary explanatory covariates.

I have advocated before that one way to mitigate problems with null - hypothesis significance testing is for editors of scientific journals to employ «results blind» decision making in determining whether to publish and make it be known that they are doing so.

(By contrast, Amrein and Berliner did no significance testing whatsoever, neglecting one of the oldest and most basic tools of social - science research.)

Readers need not get caught up in more - complicated analyses, such as significance testing, effect sizes, and even regression - statistical methods that Raymond and Hanushek criticize us for not using.

The centered PCA is bad statistics, and just because no single significance test is objectively the best in all circumstances does not mean that you can cherry pick significance tests until you find one you like and ignore R2.

Please can you identify a statistical authority (eg Cressie, Ripley etc) with a section or page number as to why it does not matter that neither of these reconstructions pass a significance test for R and yet R is widely used in similar proxy reconstructions elsewhere (including my own proxy reconstruction work)?

Both MBH98 and WahlAmman2007 fail a significance test on R. (I am aware MBH98 did not quote R, but it was shown by MM that it would fail this test).

The significance test probability of that experiment is relevant to an extraordinarily narrow conclusion about that particular experiment.

In Schmidt (2009), I used the 5 runs I had easily available, to demonstrate that the significance test that McKitrick had used vastly overstated the importance of his correlations.

«Extremely Likely» in this parlance generally matches the statistical significance test.

I agree with some of your points but, regarding null hypothesis significance testing, we're talking about datasets which we believe, a priori, will not demonstrate significance according to standard detection methods even in the presence of a trend.

Steve, unfortunately for this station the raw data ends in 1984, so it's a bit hard to say much about a difference in slope between raw and adjusted data from 1970 - 1984 (I would have to do a significance testing to see if you even have enough data records for any slope to be significant for only 15 years of observations).

Streamflow trends were statistically determined for each station using the Mann - Kendall nonparametric test at a 10 % significant level, combined with a field significance test.

We include all values, including those that do not pass a significance test.

Values that do not pass a significance test at 95 % confidence are not included.

The specific question asked was how easy is it for a trend to pass the significance test (reject null hypothesis of no trend) if that test were prompted by a record breaking event.

As I keep explaining, it depends on how the statistical significance test is used, the assumptions, on the null hypothesis, and on the conclusion.

You eyeball patterns with no significance test and no physical basis for guiding the choice of model to test against the data.

Statistical comparisons are not provided: readers do not even get a correlation coefficient, let alone a significance test.

Some google searches and a significance test?

Try performing a statistical significance test and you will find that the evidence for a the rate havin slowed is not statistically significant.

Significance test results for treatment, generation point and their interaction based on a two - way ANOVA.

EJN do not provide a significance test of the interdecadal differences in the Icelandic sea level pressures (half of the NAO) for the active versus quiet major hurricane regimes.

Significance testing gives an accepted (Settled Science) way of judging whether the observed sample satisfies the experimental or null hypothesis.

The second is on the significance test.

A 95 % significance test is standard.

Gavin's complaint about individual runs versus ensemble averages in significance testing is half - right — so he's not as ignorant as some suggest.

Scientist A conducts an experiment which yields results that meet a basic significance test.