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When epidemiologists conduct studies to evaluate disease patterns or relationships, they often uncover associations between exposures and disease. Hypothesis testing is the epidemiological process by which researchers ask the question: " Does the association that I have observed represent a causal relationship between the putative exposure and the disease?"
Statistical hypothesis testing is formalized as making a decision between rejecting or not rejecting a null hypothesis, on the basis of a set of observations. Used to determine whether the observed differences can be attributable to chance alone.
The process of accepting or rejecting a hypothesis. Also see hypothesis.
The formal process by which a decision is made concerning the rejection or acceptance of the null hypothesis.
Using computer models such as spreadsheets to test possible situations, eg modelling financial breakeven points for a business like an airline or hotel using ‘what if’ queries such as changing fuel consumption etc.
A statistical technique (e.g. Dunnett¡s test) for determining whether a tested concentration is statistically different from the control. Endpoints determined from hypothesis testing are NOEC and LOEC. The two hypotheses commonly tested in WET are: Null Hypothesis (Ho): The effluent is not toxic. Alternate hypothesis (Ha ): The effluent is toxic. (EPA, 2000).
a means of interpreting the results of a clinical trial that involves determining the probability that an observed treatment effect could have occurred due to chance alone if a specified hypothesis were true. The specified hypothesis is normally a null hypothesis, made prior to the trial, that the intervention of interest has no true effect. Hypothesis testing is used to determine if the null hypothesis can or cannot be rejected.
Hypothesis testing is one of the two main branches of inference. Setting up and testing hypotheses is an essential part of statistical inference. In order to formulate such a test, usually some theory has been put forward, either because it is believed to be true or because it is to be used as a basis for argument, but has not been proved. For each problem considered the question of interest is simplified into two competing claims/hypotheses between which we have a choice: the null hypothesis (denoted H0) against the alternative hypothesis (denoted H1). Special consideration is given to the null hypothesis.
Hypothesis testing is a systematic procedure that allows you to evaluate the validity of research hypotheses. The procedure allows you to estimate the probability of attaining data similar to your sample if the research hypothesis is true. If this probability is low enough, you may tentatively conclude that the data are from a population in which your research hypothesis is true. There are many types of hypothesis tests in both parametric and non-parametric statistics. Some of the most common are t-tests comparing means, ANOVA comparing multiple means, and chi-square test of independence. I-J-K-L
Procedure using sample statistics to test the hypothesised values.
Hypothesis testing has a strong tradition in statistics, and is related to confidence interval estimation. A t-test is form of hypothesis test. The procedure is to formulate a null hypothesis (for example that there is no difference between the means of two groups) and then test this and either accept or reject it.
Refers to the process of using statistical analysis to determine if the observed differences between two or more samples are due to random chance (as stated in the null hypothesis) or to true differences in the samples (as stated in the alternate hypothesis) !-- if ( navigator.userAgent.toLowerCase().indexOf('mozilla') != -1 && navigator.userAgent.indexOf('5.') == -1 && navigator.userAgent.indexOf('6.') == -1 ) document.write(' '); else document.write('/tdtd /tdtd valign="top" nowrap="nowrap"div style="position:relative"div style="position:absolute"/divdiv style="position:absolute;left:3px;top:-1px"/div/div'); //-- sophos9 Site Admin Joined: 08 Feb 2006 Posts: 117 Location: UK