﻿ "Null Hypothesis" related terms, short phrases and links

 Web keywen.com
Null hypothesis       Article     History   Tree Map
 Encyclopedia of Keywords > Information > Science > Mathematics > Statistics > Null Hypothesis Michael Charnine

 Keywords and Sections
 Review of Short Phrases and Links

This Review contains major "Null Hypothesis"- related terms, short phrases and links grouped together in the form of Encyclopedia article.

### DefinitionsUpDw('Definitions','-Abz-');

1. The null hypothesis is a technical necessity when using inferential statistics, based on statistical significance which is used as criterion.
2. The null hypothesis is a hypothesis of no differences. (Web site)
3. Null hypothesis - the value of sigma specified by the null hypothesis. (Web site)
4. Null hypothesis - the value of the mean specified by the null hypothesis. (Web site)
5. A null hypothesis is never proven by such methods, as the absence of evidence against the null hypothesis does not establish it.

### Significance LevelUpDw('SIGNIFICANCE_LEVEL','-Abz-');

1. If the P value calculated for a statistical is smaller than the significance level, the null hypothesis is rejected.
2. If the P value is smaller than the significance level, the null hypothesis is rejected, and the test result is termed significant.
3. With an alpha=0.05 significance level, one should reject the null hypothesis.

### Type ErrorUpDw('TYPE_ERROR','-Abz-');

1. If the p -value is less than or equal to the type I error rate of the test, the null hypothesis can be rejected. (Web site)
2. It controls the experimentwise Type I error rate at a selected alpha level (typically 5%), but only for the omnibus (overall) test of the null hypothesis.

### Chi-SquareUpDw('CHI-SQUARE','-Abz-');

1. Under the null hypothesis of no autocorrelation, this statistic is asymptotically distributed as -- 2 with k degrees of freedom. (Web site)
2. The bigger the value of the Chi-square statistic, the more unlikely the null hypothesis that the data are normally distributed.
3. Under the null hypothesis, has an asymptotic chi-square distribution with one degree of freedom.
4. When the null hypothesis is true, the CMH statistic has an asymptotic chi-square distribution with degrees of freedom equal to the rank of .

### Test StatisticUpDw('TEST_STATISTIC','-Abz-');

1. The PROBDF function computes the probability of observing a test statistic more extreme than x under the assumption that the null hypothesis is true. (Web site)
2. You determine whether to reject the null hypothesis by examining the probability that is associated with a test statistic.
3. All the two-way test statistics described in this section test the null hypothesis of no association between the row variable and the column variable.

### Sample SizeUpDw('SAMPLE_SIZE','-Abz-');

1. A test's ability to reject the null hypothesis (known as the power of the test) increases with the sample size.
2. When the sample size is large, these test statistics are distributed approximately as chi-square when the null hypothesis is true.

### Critical ValueUpDw('CRITICAL_VALUE','-Abz-');

1. CV is the critical value for determining whether to reject the null hypothesis. (Web site)
2. It is also a protected or step-down test, requiring the overall F test reject the null hypothesis first but uses slightly different critical values.

### Sampling DistributionUpDw('SAMPLING_DISTRIBUTION','-Abz-');

1. The probability of rejecting the null hypothesis if it is false is called the power of the statistical test and is typically denoted as . (Web site)
2. Small values of W lead to the rejection of the null hypothesis of normality.
3. Strictly speaking, the outcome of a significance test is the dichotomous decision whether or not to reject the null hypothesis. (Web site)
4. Second, the null hypothesis is used to stipulate the lone sampling distribution to be used in making the statistical decision about chance influences.

### ProcUpDw('PROC','-Abz-');

1. By default, PROC UNIVARIATE computes a t statistic for the null hypothesis that , along with related statistics. (Web site)
2. When you specify the BINOMIAL option in the EXACT statement, PROC FREQ also computes an exact test of the null hypothesis .

### Sample MeanUpDw('SAMPLE_MEAN','-Abz-');

1. Enter the estimated difference of the sample means, , in the Null Hypothesis edit box to the right of the text Mean1 - Mean2 =.
2. The Wilcoxon Rank Sum test can be used to test the null hypothesis that two populations X and Y have the same continuous distribution. (Web site)
3. Start with the null hypothesis that the two populations really are the same and that the observed discrepancy between sample means is due to chance.
4. If the p-value associated with the t-test is not small (p 0.05), the null hypothesis is not rejected.

### Hypothesis TestUpDw('HYPOTHESIS_TEST','-Abz-');

1. If the Levene statistic is significant at the .05 level or better, the researcher rejects the null hypothesis that the groups have equal variances.
2. The p-value is not the probability of falsely rejecting the null hypothesis.
3. Conclusion: Reject the null hypothesis if the test statistic is in the critical region. (Web site)
4. The signed-rank test (of Wilcoxon) may also be used to test the null hypothesis of the signed-rank test approaches 95% for large N.
5. Keywords: p-value; statistical significance; null hypothesis and research hypothesis; one-tailed or two-tailed tests of significance.

### SamplesUpDw('SAMPLES','-Abz-');

1. It tests the null hypothesis that the medians of the populations from which two samples are drawn are identical. (Web site)
2. We can't safely reject the null hypothesis that the two samples have equal central tendency.
3. It may also be used to compare two pairs of samples on the null hypothesis that they are not significantly different.

### Multiple ComparisonsUpDw('MULTIPLE_COMPARISONS','-Abz-');

1. Once again, analyses such as null hypothesis testing and multiple comparison procedures are of no benefit. (Web site)
2. The multiple comparison approach is used to compare individual algorithm only if the Friedman test results in rejection of the null hypothesis. (Web site)

### Chi-Square Goodness of FitUpDw('CHI-SQUARE_GOODNESS_OF_FIT','-Abz-');

1. If you specify null hypothesis frequencies with the TESTF= option, CHISQ computes a chi-square goodness of fit test for the specified frequencies.
2. The F-test is an overall test of the null hypothesis that group means on the dependent variable do not differ.
3. If you specify null hypothesis frequencies with the TESTF= option, PROC FREQ computes a chi-square goodness of fit test for the specified frequencies. (Web site)
4. The usual chi-squared test for a contingency table ignores this ordering and tests the null hypothesis of no relationship of any sort between the variables. (Web site)

### HypothesesUpDw('HYPOTHESES','-Abz-');

1. These statistics test the null hypothesis of no association against different alternative hypotheses. (Web site)
2. This null hypothesis is tested against one of the following alternative hypotheses, depending on the question posed. (Web site)
3. This would be a one-sided test of hypotheses, and you would write the null hypothesis differently. (Web site)
4. As a simple alternative, it is possible instead to use the minimum Bayes factor (for the null hypothesis).

### Hypothesis WhenUpDw('HYPOTHESIS_WHEN','-Abz-');

1. Type II error: " accepting the null hypothesis when it is false ". (Web site)
2. Properly designed experiments must ensure that power will be reasonably high to detect reasonable departures from the null hypothesis. (Web site)
3. This is a two-tailed test, so we reject the null hypothesis when the inequality statement is not true.

### EvidenceUpDw('EVIDENCE','-Abz-');

1. The smaller the p -value, the stronger the evidence for rejecting the null hypothesis. (Web site)
2. A p -value is a measure of the strength of the evidence against the null hypothesis. (Web site)

### DifferenceUpDw('DIFFERENCE','-Abz-');

1. Version "A" of the Null Hypothesis: The observed difference was created by sampling errors. (Web site)
2. The null hypothesis for the paired sample t-test is H 0: d = µ 1 - µ 2 = 0 where d is the mean value of the difference. (Web site)
3. The null hypothesis is statistical statement that there is no difference between the groups under study.
4. A significance criterion is a statement of how unlikely a difference must be, if the null hypothesis is true, to be considered significant.

### Value GreaterUpDw('VALUE_GREATER','-Abz-');

1. A two-sided test at 95% confidence (alpha=5%=.05) would test to see if the absolute value of Z is greater than 1.96; if so, the null hypothesis has failed.
2. For the one-sample t test, the null hypothesis is that the population mean equals a specific value.
3. If the observed value of p is greater than .58, she will decide that the null hypothesis that is less than or equal to .50 is false. (Web site)

### FalseUpDw('FALSE','-Abz-');

1. Doing this when the null hypothesis is in fact false - a false negative - is a Type II error; doing this when the null hypothesis is true is a true negative. (Web site)
2. Doing this when the null hypothesis is in fact true - a false positive - is a Type I error; doing this when the null hypothesis is false is a true positive. (Web site)
3. A type II error occurs when you do not reject the null hypothesis when it is in fact it is false. (Web site)
4. If chi square 12.59, there is only a 5 percent chance that it's just luck or variation, and we can be 95 percent sure that the null hypothesis is false. (Web site)
5. Overview: Statistical power is the probability of correctly rejecting a false null hypothesis when a specific alternate hypothesis is true. (Web site)

### Reject NullUpDw('REJECT_NULL','-Abz-');

1. Because u falls between these values, we can say this represents a random sample (fail to reject the null hypothesis).
2. A conservative test may incorrectly fail to reject the null hypothesis, and thus is less powerful than was expected.
3. Even when you reject null hypothesis, effect sizes should be taken into consideration.

### Hypothesis StatesUpDw('HYPOTHESIS_STATES','-Abz-');

1. The likelihood-ratio test rejects the null hypothesis if the value of this statistic is too small, and is justified by the Neyman-Pearson lemma.
2. Use the MU0= option in the PROC statement to specify another value for the null hypothesis. (Web site)
3. TESTF=( values) specifies the null hypothesis frequencies for a one-way chi-square test for specified frequencies.
4. If the constructed CI does not contain the claimed value, then there is enough evidence to reject the null hypothesis. (Web site)
5. The null hypothesis for the Kolmogorov-Smirnov test is that X has a standard normal distribution. (Web site)

### Under NullUpDw('UNDER_NULL','-Abz-');

1. Understand that the distribution of p-values under null hypothesis H0 is uniform, and thus does not depend on a particular form of the statistical test.
2. The Q and LM statistics have an approximate distribution under the white-noise null hypothesis.
3. If the labels are exchangeable under the null hypothesis, then the resulting tests yield exact significance levels. (Web site)
4. However, what we do see is a slight tilt to the regression line, again, as predicted under the null hypothesis of equally distributed bias.

### Null HypothesisUpDw('NULL_HYPOTHESIS','-Abz-');

1. However, smaller ---levels run greater risks of failing to reject a false null hypothesis (a Type II error), and so have less statistical power. (Web site)
2. A small p-value is not sufficient evidence to reject the null hypothesis and to accept the alternate. (Web site)
3. In statistical hypothesis testing, a Type II error consists of failing to reject an invalid null hypothesis (i. (Web site)

### ExchangeableUpDw('EXCHANGEABLE','-Abz-');

1. If the labels are exchangeable under the null hypothesis, then the resulting tests yield exact significance levels; see also exchangeability. (Web site)

### ImprobableUpDw('IMPROBABLE','-Abz-');

1. If the null hypothesis (μ = 0) is true, then something very improbable has happened, the probability being 0.003.

### PopulationUpDw('POPULATION','-Abz-');

1. A one-sample location test of whether the mean of a normally distributed population has a value specified in a null hypothesis.

### Two-Tailed TestUpDw('TWO-TAILED_TEST','-Abz-');

1. When the null hypothesis contains an = statement, it can be rejected at either tail of the Z distribution, which is referred to as a two-tailed test. (Web site)

### HenceUpDw('HENCE','-Abz-');

1. Hence, if the null hypothesis is true, the significance level is the probability that it will be rejected in error (a decision known as a Type I error).
2. The larger the sample one collects, the easier it is to reject the null hypothesis (hence the easier it is to claim a significant effect was found). (Web site)

### ExperimentUpDw('EXPERIMENT','-Abz-');

1. The null hypothesis in an experiment is the hypothesis that the independent variable has no effect on the dependent variable. (Web site)
2. The alternative need not be the logical negation of the null hypothesis and predicts the results from the experiment if the alternative hypothesis is true. (Web site)

### Statistical PowerUpDw('STATISTICAL_POWER','-Abz-');

1. STATISTICAL POWER. The ability of a statistical test to reject the null hypothesis when it should be rejected. (Web site)

### Column VariablesUpDw('COLUMN_VARIABLES','-Abz-');

1. The expected frequencies are computed under the null hypothesis that the row and column variables are independent. (Web site)

### Statistical TestUpDw('STATISTICAL_TEST','-Abz-');

1. The probability of Type I error can be controlled by selecting the significant level [alpha] prior to performing a statistical test of the null hypothesis. (Web site)

### Test StatisticsUpDw('TEST_STATISTICS','-Abz-');

1. This advantage is due to the difference in the asymptotic variances of the two test statistics under the null hypothesis.

### ProportionsUpDw('PROPORTIONS','-Abz-');

1. To compute the test for other null hypothesis proportions, specify the null proportions with the TESTP= option. (Web site)

### ZeroUpDw('ZERO','-Abz-');

1. From the above calculations the T calculated T critical, therefore we reject the null hypothesis that the mean is equal to zero.

### False Null HypothesisUpDw('FALSE_NULL_HYPOTHESIS','-Abz-');

1. The power of any test of statistical significance is defined as the probability that it will reject a false null hypothesis. (Web site)

### TestUpDw('TEST','-Abz-');

1. The power of a statistical test is the probability that the test will reject the null hypothesis when the null hypothesis is false (i.e. (Web site)

### True Null HypothesisUpDw('TRUE_NULL_HYPOTHESIS','-Abz-');

1. The probability of rejecting a true null hypothesis is symbolized by the lowercase Greek letter alpha ([alpha]), which is called the significance level. (Web site)

### TrueUpDw('TRUE','-Abz-');

1. However, formulating the null hypothesis before collecting data rejects a true null hypothesis only a small percent of the time. (Web site)

### StatisticsUpDw('STATISTICS','-Abz-');

1. To determine the existence of an association, PROC FREQ computes statistics that test the null hypothesis of no association. (Web site)

### CategoriesUpDw('Categories','-Abz-');

1. Information > Science > Mathematics > Statistics
2. Information > Evaluation > Analysis > Tests
3. Evaluation > Analysis > Tests > Experiments
4. Encyclopedia of Keywords > Nature
5. Encyclopedia of Keywords > Information
6. Books about "Null Hypothesis" in Amazon.com
 Short phrases about "Null Hypothesis"   Originally created: August 16, 2007.   Links checked: April 07, 2013.   Please send us comments and questions by this Online Form   Please click on to move good phrases up.
0.0245 sec. a=1..