
Review of Short Phrases and Links 
This Review contains major "PValue" related terms, short phrases and links grouped together in the form of Encyclopedia article.
Definitions
 A pvalue is defined as the probability of rejecting the null hypothesis {The interaction gene x condition is null.}, if true.
 A pvalue is a measure of how much evidence you have against the null hypothesis.
 The Pvalue is the probability that a zscore will be less than 2.0.
 The pvalue is the twotailed probability computed using a t distribution.
 The pvalue is based on the assumption that the distribution is normal.
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 Now the pvalue is approximately 0.13, so we do not reject the hypothesis.
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 P is the pvalue of the test, obtained by linear interpolation in a set of table created by Lilliefors.
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 Another way to report the results of a statistical test is to compute a probability value or pvalue.
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 However, the pvalue is smaller for the onesided test.
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 In the end, most folks summarize the result of a hypothesis test into one particular value  the pvalue.
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 STAR2 = upper bound on pvalue for printing two stars (**), when STARS option is on.
 The small pvalue of 0.001 indicates the popcorn brand affects the yield of popcorn.
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 The first pvalue tests whether the dimension is 0, the next whether the dimension is 1, and so on.
 If the pvalue is not low, there will be a fair amount of overlap between the two groups.
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 The pvalue can then be used to make a statement as to whether or not the two data sets are significantly different.
 If a test of significance gives a pvalue lower than the level, the null hypothesis is rejected.
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 The null hypothesis is that the data set is similar to the normal distribution, therefore a sufficiently small Pvalue indicates nonnormal data.
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 One may combine the pvalue with the significance level to make decision on a given test of hypothesis.
 The pvalue is not the probability of falsely rejecting the null hypothesis.
 If alpha is omitted, the test statistic is printed and a range for the pvalue of the test is given.
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 As might be expected, the rank sum test leads to the same conclusion but it is less sensitive to the difference between samples (higher pvalue).
 The sum of these approximates the pvalue of the test statistic for the two sided alternative.
 If the pvalue is large, then this indicates that the test statistic is usual (common)we take this as a lack of evidence against the null hypothesis.
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 Since we have a onetailed test, the Pvalue is the probability that the zscore is greater than 1.91.
 The equal variances ttest has a pvalue of 0.213.
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 The ttest turns the difference in mean cholesterol into a pvalue.
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 We can also draw this conclusion by comparing the pvalue for a twotail test and the alpha value.
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 Or, if the pvalue is greater than or equal to alpha, then the difference between the numbers could be due to chance alone.
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 The pvalue is determined by the observed value, however, this makes it difficult to even state the inverse of p.
 This number is used to compute the approximate finite sample Pvalue or critical value.
 The chisquare value was 583.3 with degrees of freedom 215 (Pvalue < 0.0001).
 The specification by year interaction for the #200 sieve had a pvalue = 0.03.
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 All the other sieve sizes, including the #200 sieve, had a pvalue greater than 0.05.
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 In one example, with three lags, a value of 3.17 constituted rejection at the pvalue of .10.
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 Based on where the rejection region is, you must find out what pvalue to use.
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 Use of Pvalue and a: In this setting, we must also consider the alternative hypothesis in drawing the rejection interval (region) .
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 The Pvalue indicates how likely it is that we would obtain a tstatistic as large as that observed if the null hypothesis is true.
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 Bstatistic, tstatistic and Pvalue (probability) are generated using the R statistical language with Bioconductor and the LIMMA package.
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 A pvalue = 0.51 in the ANOVA table indicated that there was no change in VMA variability.
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 The only statistically significant difference, however, is between 1998 and 2000, which is indicated by a pvalue = 0.02.
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 Keywords: Multiple hypothesis testing; adjusted pvalue; familywise Type I error rate; false discovery rate; permutation; DNA microarray.
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 The Pvalue is obtained from moderated tstatistic and after FDR adjustment which is Benjamini and Hochberg's method to control the false discovery rate.
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PValue
 If the pvalue is significant, appropriate posthoc multiple comparisons tests would be performed.
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 Example of filters include: number of missing value, coefficient of variation of expression measures, ANOVA pvalue, Cox model pvalues.
 A working paper that explains the difference between Fisher's evidential pvalue and the NeymanPearson Type I error rate .
 The next table gives the Mantel r statistic, and the corresponding twotailed pvalue, obtained from the 10000 permutations.
 To do this, the treatment means need to be looked at (Oehlert, 2000).Unlike interaction plots, a pvalue is associated with a contrast.
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Categories
 Information > Evaluation > Analysis > Tests
 Information > Science > Mathematics > Statistics
 Glossaries > Glossary of Statistics /

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