Sentences with phrase «statistical significance tests»

Statistical significance tests?

Statistical significance tests.

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

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

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

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

Not exact matches

However, an exponential distribution provides a more stringent and conservative test for statistical significance than either a normal distribution or a Poisson distribution.

The overall test for interaction (heterogeneity) was of borderline statistical significance for all women (P = 0.06), and was significant for women with no complicating conditions at the start of care in labour (P = 0.03).

All statistical tests were two - sided and conducted using an α level of 0.05 to judge significance.

Statistical tests of significance such as chi - square tests were used for ordinal and categorical variables.

The 2 - tailed Student's t - test was used to assess the statistical significance of our results.

I can see that you're a big fan of relative risk, but what you're not acknowledging is that there are tests of statistical significance that researchers run in order to determine whether any difference between two outcomes is real or not.

You'll want to wait for what's called «statistical significance,» a fancy term that means that there's a 95 % chance that the results are representative of what would happen if you let the test go on forever.

Fortunately, for us and the rest of the busy world, an online calculator called AB / BA will calculate the statistical significance of your test results.

Analysis of these data in the overall intent to treat population showed an advantage in PFS favouring arm A that did not reach statistical significance; Median PFS was 6.4 months in the FOLFOX plus cetuximab arm A compared to 4.5 months in the FOLFOX arm B (hazard ratio [HR] 0.81; 95 % CI 0.58, 1.12; log - rank test, p = 0.19).

But in most such instances the statistics applied in court have been primarily the standard type that scientists use to test hypotheses (producing numbers for gauging «statistical significance»).

For retention block 4, we compared the median latency and median excess path length for navigation to store locations that had been learned while stimulation was applied with those learned in the absence of stimulation during blocks 1, 2, and 3, using the Wilcoxon signed - rank test, with a P value of less than 0.05 considered to indicate statistical significance.

When it comes to statistical significance and hypothis testing, I do not recall whether the trends have been tested against a null hypothesis, but the short - term variability is quite high compared to the trend in the WM2003 case and the series is short, so I doubt the «trend» is significant (just by eyeballing).

Asterisks indicate statistical significance by two - tail t test (n = 3, P < 0.05).

The statistical significance (P < 0.05 by t - test) is indicated by an asterisk (*) and ($).

Asterisk indicates statistical significance by two - tail t test (n = 3, P < 0.05).

Aggregation tests (sometimes called burden tests) were developed to help identify genetic association driven by variants that, individually, are too rare to reach statistical significance on their own.

A two - sample T - test was applied to assess statistical significance (alpha = 0.05).

For 2 - group comparisons, a 2 × 2 Yates corrected χ2 test was used to evaluate the statistical significance of group differences of percentages of unscored applications and percentages of funded applications.

Statistical significance was measured using parametric testing, assuming equal variance, in the majority of experiments with standard t tests for 2 paired samples used to assess difference between test and control samples.

The differences in percentage of marital break - ups across on - line venues approached statistical significance [χ2 (10) = 16.71, P = 0.08; Table S5], but differences across off - line venues were not statistically significant [χ2 (9) = 10.17, P = 0.34], and neither test was significant after controlling for covariates [χ2 (10) = 14.41, P = 0.17, and χ2 (9) = 7.66, P = 0.56, respectively].

When testing the statistical significance of differences between sectors, however, we take into account the full distribution of responses across all options.

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.

Other states have added a test of statistical significance to increase the certainty of their decisions.

The purpose of tests of statistical significance is to determine whether results reflect genuine changes in performance or simply random fluctuation.

We know of no legitimate statistical text that argues it is irrelevant to use tests of statistical significance to guard against random fluctuations in the data - in this case, scores on tests of student performance.

All comparisons are subjected to testing for statistical significance, and estimates of standard errors are computed for all statistics.

Almost all of the factors and smart beta strategies exhibit a negative relationship between starting valuation and subsequent performance whether we use the aggregate measure or P / B to define relative valuation.9 Out of 192 tests shown here, not a single test has the «wrong» sign: in every case, the cheaper the factor or strategy gets, relative to its historical average, the more likely it is to deliver positive performance.10 For most factors and strategies (two - thirds of the 192 tests) the relationship holds with statistical significance for horizons ranging from one month to five years and using both valuation measures (44 % of these results are significant at the 1 % level).

Most strategies produce results which pass tests of statistical significance at 95 % confidence.

You should reject all claims that an effect does not exist simply because a statistical test fails to declare significance.

If we test whether or not they are, en mass, positive or negative, we can't rely on the super-strong statistical significance of a t test because they're obviously not following the normal distribution.

Rather, we should rely on the super-strong statistical significance of the non-parametric Wilcoxon rank sum test.

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)?

It seems to be before we can discuss statistical tests of any data set for the significance of any hypothesis, we must test the data itself for various characteristics.

In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance.

Thus, we can be sure that the level of statistical significance would be abyssmal if the climate models were tested.

it is important to recognize that an inherent difficulty of testing null hypotheses is that one can not confirm (statistically) the hypothesis of no effect.While robustness checks (reported in the appendix), as well as p values that never approach standard levels of statistical significance, provide some confidence that the results do not depend on model specification or overly strict requirements for statistical significance, one can not entirely dismiss the possibility of a Type II error.

It turns out that the A&W Climatic Change depends on their GRL submission for their test of statistical significance for the RE statistic.

Statistical significance between the experiments and the control is tested using a student's t - test and significance is determined using resultant p values, where a p value less than or equal to 0.05 allows rejecting the null hypothesis that the differences should be zero.

The failure of the test for statistical significance in a time series does not allow the conclusion that a trend is absent.And «stopped» means the same as absent.

However, by arguing in this way Courtney has only proven that he is absolutely clueless with respect to statistical analysis and the conclusion that can validly be drawn from the fact that a test for statistical significance fails.

Working from an example, we show how a composite may be objectively constructed to maximize signal detection, robustly identify statistical significance, and quantify the lower - limit uncertainty related to hypothesis testing.

Statistics is pretty much based on the random selection from a population and the tests of statistical significance generally tend to be based on the assumption that the population is normally distributed (gaussian if your into hiding simple ideas behind the names of dead mathematicians).

Temperature anomalies are arrived at via statistical methods; therefore it is necessary to test for significance, because the results might be statistical accidents.

But in any case there's no place in an individual case for T - tests and statistical significance, which is the point I am making.