# significance level

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## significance level

In statistics, the point at which a null hypothesis is rejected. Also known as the "level of significance." Who can identify the significance level in this problem?
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Out of the variables in Table 5, it is empirically observed that the first lagged terms of [DELTA]LBALEPF, [DELTA]LPOP60, [DELTA]LMORT and a constant term, which have positive and negative coefficients, are statistically significant against [DELTA]LBALEPF at the significance levels of five percent and 10 percent, respectively in the short run.
For different [delta] values and significance levels, detect the pollution at the initial observation time 0.01 h, and the results are shown in Table 3.
But before we talk about effect sizes, let us take a step back and look at significance levels. Without getting into the nitty gritty of hypothesis testing, what any statistical test is trying to determine is whether, given the data at hand, the results are more or less likely to be due to chance or to some real underlying cause-and-effect situation.
The significance levels of all sub-scales are 0.0001 which is less than %5 and hence indicate that there is a meaningful relationship between all the sub-scales and the effectiveness of advertising.
When rainfall volume per sub-basin was provided (Figure 11b), the sub-basins did not show any significant trend by the Mann-Kendall's test that would prove a decrease or an increase of rainfall volume per region, regardless of the significance levels used.
The table presents the critical win numbers for various dominance thresholds, numbers of contests, and significance levels, using the one-firm test.
Misreporting could also affect the variance of the estimated impacts, and hence, significance levels from statistical hypothesis tests of intervention effects.
1009) Before 2009 t= 11.37 (0.000) *** After 2008 Nonparamctric test (Mann.Whitney Test): Z-value = 11.8763 (0.000) *** Notes: The p-values are shown in parentheses, *, **, *** and *** denote statistical significance at the 10%, 5%, 1%, and 0.1% significance levels, respectively.
Sample sizes were 25, 100, and 400, and the significance levels were .05 and .01.
Common assumptions of this approach are significance levels of 1% , 5%, or 10% and no dependency on past performance k = 0.
The result of this correction was that none of the geographic predictors employed in the regressions retained conventional significance levels.
The [chi square] tests also show that 270 is in significant linkage disequilibrium with the other 6 SNP (128, 178, 241, 312, 328, and 371) at significance levels 4.5e-23 [less than or equal to] p [less than or equal to] 5.7e-07 (data not shown), whereas any 2 of the other 6 SNP markers were or were not in significant linkage disequilibrium statistically with [chi square] test.
I will leave out the full results and go right to the table of significance levels:
Data were analyzed using AMOS 4.0 to obtain regression weights, standardized regression weights, squared multiple correlations for the endogenous variables, and significance levels for test statistics.
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