A tentative explanation which indicates that a chance distribution is operating; a contrast to the null hypothesis.
In the ANOVA context, a statement that there is no difference among a set of true means. In other statistical settings, the statement similarly refers to an absence of interesting differences. Because observed means are estimates of the true values, statistical machinery is invoked to determine the validity of the hypothesis. The fallibility of data implies that some incorrect decisions are inevitable.
The idea that the causes, effects, amounts, or changes in question (the study “variables”) are not really connected to each other at all. This hypothesis is the opposite of the research hypothesis. pen-ended questions – Questions which let people answer in their own words instead of having to choose from set answers like “a” or “b” or “true” or “false.
A statement that something does not exist, or can not happen, or is not related. These statements cannot be proven.
A hypothesis that any difference between conditions occurs by chance. Rejection of the null hypothesis means that within some probability the treatment conditions are different.
in a statistical test it is the statement of the hypothesis to be tested.
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. Investigators usually hope that the data will demonstrate some effect from the intervention, thereby allowing the investigator to reject the null hypothesis.
The hypothesis that an observed pattern of data and an expected pattern are effectively the same, differing only by chance, not because they are truly different. A statistical significance test is then generally applied to the data to test whether the hypothesis can be rejected. If so, the observed and expected patterns are said to be significantly different. Tests do not establish that the null hypothesis is true. 'Expected' patterns may be derived from theory or from other, related data sets.
The statistical hypothesis tested by the statistical procedure. Usually a hypothesis of no difference or no relationship.
the prediction that there is no relationship between your treatment and your outcome.
The assumption that an observed difference is due to chance rather than some other, hypothesized causal factor
a hypothesis which, if rejected, indicates that the reverse is true and the relationship is considered to exist.
The assumption that there is no significant difference between two study groups.
The hypothesis that "experimental" results are due to "chance".
the reverse of the research hypothesis. The null hypothesis is directly tested by statistical analysis so that it is either rejected or not rejected, with a confidence level. If the null hypothesis is rejected, the alternative hypothesis is supported.
A formal statement that there is no difference or no relationship between variables. Researchers often use the results of statistical test to reject the null hypothesis.
What do you do when you want others to be maximally impressed with what you do? You DECREASE EXPECTATIONS, then what you do accomplish looks even better! The null hypothesis is the assumption that there is no difference between the groups and that the treatment you are studying has no effect. Any difference in outcome actually observed between the groups is then evaluated in relationship to the "zero expectation" hypothesis.
A statement expressing no relation or no difference between two (or more) variables.
a statement that assumes null effect e
a statistical hypothesis that is tested for possible rejection under the assumption that it is true (usually that observations are the result of chance)
usually denoted Ho. A statement about a probabilistic model that represents the opposite of what one wants to demonstrate. The statement must be sufficiently precise to allow the computation of probabilities.
the prediction that is tentatively held to be true; it states that no relationship will be found between two variables, or that the means of multiple groups are equal.
This is usually a statement of "no effect", that is to say that the independent variable will not have any effect on the dependent variable and that any differences between the experimental and control groups are attributable to chance. The null hypothesis is usually represented by the symbol H0, and is stated in order that it can be rejected as an explanation for the results of the experiment.
The assumption that there is no true difference between groups and any difference (statistically) is due to sampling errors. A researcher tries to disprove this
The hypothesis in a study that asserts that there is no difference between groups or relationship between variables. The statistician normally poses the null hypothesis (Ho) and tests it statistically. If it is rejected, the alternative hypothesis (Ha) that there is a relationship between variables is accepted.
in hypothesis testing, the hypothesis that an intervention has no effect, i.e., that there is no true difference in outcomes between a treatment group and a control group. Typically, if statistical tests indicate that the value is at or above the specified a-level (e.g., 0.01 or 0.05), then any observed treatment effect is not statistically significant, and the null hypothesis cannot be rejected. If the P value is less than the specified a-level, then the treatment effect is statistically significant, and the null hypothesis is rejected. If a confidence interval (e.g., of 95% or 99%) includes zero treatment effect, then the null hypothesis cannot be rejected.
In a statistical sense, to the statement that there will not be any difference between effects of experimental treatments.
The primary hypothesis being investigated; often it is an hypothesis that there is no difference between two values, or that a sample value is not different from zero.
The null hypothesis is used in experimental research. It asserts arbitrarily that there is no relationship among the variables being studied. Then statistical tests are used to determine if any relationship shown by the research data is due to chance alone or to alternative hypotheses.
The hypothesis of no difference or no differential effects.
Specifies the value of the parameter (difference between two treatments) that needs to be tested (difference between two treatments = 0).
Statement of no change or difference. The default is to assume the Null Hypothesis to be true unless refuted by sufficient evidence. If the Null Hypothesis is refuted, the Alternate Hypothesis is accepted instead, with a certain level of confidence.
A precise statement relating to the research question to be tested, expressed in terms which assume no relationship (association) or difference between variables. Used in conjunction with a suitable alternate hypothesis. (See the issues of quantitative analysis for more information.)
The statistical hypothesis that states there is no measurable difference between the observed information and measured data, i.e., it assumes that the treatments have no effect.
the prediction that the independent variable will have no effect on the dependent variable in an experiment. (651)
A precise statement that makes the opposite prediction about the results of a study as the experimental hypothesis. The purpose of the study is to test this prediction. More specifically, the inferential statistics used test the 'truth' of the null hypothesis.
A hypothesis that asserts either the equivalence of two unknown quantities or the positive statement of a condition (for example, "mean 1 is equal to mean 2" or "the population is normal"). It is used in hypothesis testing as the default hypothesis in contrast to the alternative hypothesis.
The hypothesis being tested about a population. Null generally means "no difference" and thus refers a situation in which there is no difference (e.g., between the means in a treatment group and a control group).
The statistical hypothesis that states that there are no differences between observed and expected data.
In statistics, a null hypothesis is a hypothesis set up to be nullified or refuted in order to support an alternative hypothesis. When used, the null hypothesis is presumed true until statistical evidence in the form of a hypothesis test indicates otherwise. The use of the null hypothesis is controversial; a null hypothesis is often the reverse of what the experimenter actually believes; it is put forward to allow the data to contradict it.