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a supposition that appears to explain a group of phenomena and is advanced as a bases for further investigation.
alternative hypothesis the hypothesis that is formulated as an opposite to the null hypothesis in a statistical test.
complex hypothesis a prediction of the relationship between two or more independent variables and two or more dependent variables.
directional hypothesis a statement of the specific nature (direction) of the relationship between two or more variables.
Lyon hypothesis a hypothesis about development of X chromosomes in the embryo; see lyon hypothesis.
Monro-Kellie hypothesis [mun-ro´ kel´e] an explanation of the maintenance of intracranial pressure: The skull is viewed as a closed container housing brain tissue, blood, and cerebrospinal fluid; a change in any of these three components will affect the other two. If the volume added to the cranial vault is equal to the volume displaced, the intracranial volume will not change.
nondirectional hypothesis a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.
null hypothesis the hyothesis that the effect, relationship, or other manifestation of variables and data under investigation does not exist; an example would be the hypothesis that there is no difference between experimental and control groups in a clinical trial.
hypothesis test the abstract procedure that is the theoretical basis of most statistical tests. A hypothesis test decides between two hypotheses, the null hypothesis (H0) that the effect under investigation does not exist and the alternative hypothesis (H1) that some specified effect does exist, based on the observed value of a test statistic whose sampling distribution is completely determined by H0. The decision is made to reject H0 and by implication to accept H1 when the test statistic falls within a given set of values called the critical region. This region is so determined that the probability of rejecting H0 when it is in fact true (a so-called Type I error, the reporting as significant results that are only the result of random variation and not a real effect), is set at a specified level (symbol α). When this level is set before the data are collected, usually at 0.05 or 0.01, it is called the significance level or α level. It is now more common to report the smallest α at which the null hypothesis can be rejected; this is called the significance probability or P value. The ability of the test to accept a true alternative (and thus to detect a real effect when it exists) is termed the power of the test. Note that no statistical test actually tests the H1.
the statistical hypothesis that one variable has no association with another variable or set of variables, or that two or more populations do not differ from each other; the statement that results do not differ from those that might be expected by the operation of chance alone; if rejected, it increases confidence in the hypothesis.
A hypothesis that asserts that if there are no differences between 2 populations—or sets of data being compared—a statement of probabilities (P value) can be made. The proposition to be tested statistically is that the experimental intervention has "no effect," meaning that the treatment and control group outcomes are the same despite the intervention. If the null hypothesis is true, then a study’s findings are due to chance or random factors. The purpose of a typical study is to "reject the null hypothesis." The null hypothesis is a statistical assumption based on data which demonstrates an association of 2 events or factors in > 95% of cases. Said another way, the researcher wants to prove that there is less than a 1 in 20 chance that the differences between 2 treatments in a trial could have occurred by chance—i.e., less than a 1 in 20 chance that the null hypothesis is true.
The proposal that there is no difference between groups or no association between a risk factor and an outcome.
null hypothesisStatistics A hypothesis that assumes that if there are no differences between 2 populations–or sets of data being compared, a statement of probabilities–P value can be made; the proposition, to be tested statistically, that the experimental intervention has "no effect," meaning that the treatment and control groups will not differ as a result of the intervention. The NH is a statistical assumption based on data which demonstrates an association of 2 events or factors in > 95% of cases. See Hypothesis testing. Cf Alternative hypothesis.
null hy·poth·e·sis(nŭl hī-poth'ĕ-sis)
The statistical hypothesis that one variable has no association with another, or that experimental results do not differ from those that might be expected by the operation of chance alone.
null hypothesisThe assumption that one variable has no effect on another variable, or that only one hypothesis can possibly account for a phenomenon. The assumption that there are no differences in two population in matters relevant to the current investigation. In a clinical trial of a new treatment the null hypothesis might be that the proportion of patients improved by it was the same as the proportion improved by the existing standard treatment. See HYPOTHESIS TEST.
null hypothesis (NH)a statement that a certain relationship exists, which can be tested with a statistical SIGNIFICANCE test. A typical null hypothesis is the statement that the deviation between observed and expected results is due to chance alone. In biology, a probability of greater than 5% that the NH is true (P 5%) is considered acceptable.
null hy·poth·e·sis(nŭl hī-poth'ĕ-sis)
Statistical proposition that one variable has no association with another variable or set of variables, or that two or more populations do not differ from each other; the statement that results do not differ from those that might be expected by the operation of chance alone; if rejected, it increases confidence in the hypothesis.