Examples of using Statistical hypothesis in English and their translations into Ukrainian
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Role in statistical hypothesis testing.
An alternative to statistical hypothesis tests called estimation statistics.
Pearson is best known for development of the Neyman- Pearson lemma of statistical hypothesis testing.
A statistical hypothesis that refers only to the numerical values of unknown parameters of a distribution is called a parametric hypothesis. .
The term is loosely used for themodern version which is now part of statistical hypothesis testing.
The statistical hypothesis test added mathematical rigor and philosophical consistency to the concept by making the alternative hypothesis explicit.
The term is loosely used to describe themodern version which is now part of statistical hypothesis testing.
To be a real statistical hypothesis test, this example requires the formalities of a probability calculation and a comparison of that probability to a standard.
A choice of small sample sizes, though sometimes necessary,can result in wide confidence intervals or risks of errors in statistical hypothesis testing.
The Kendall rank coefficientis often used as a test statistic in a statistical hypothesis test to establish whether two variables may be regarded as statistically dependent.
In statistics, every conjecture concerning the unknown distribution F{\displaystyle F} of a random variable X{\displaystyle X}is called a statistical hypothesis.
Using a medical treatment as an example, a statistical hypothesis might attempt to illustrate, with statistical significance, whether a drug performs better than a placebo.
Information visualization is also a hypothesis generation scheme, which can be, and istypically followed by more analytical or formal analysis, such as statistical hypothesis testing.
An alternative framework for statistical hypothesis testing is to specify a set of statistical models, one for each candidate hypothesis, and then use model selection techniques to choose the most appropriate model.
The p-value refers only to a single hypothesis, called the null hypothesis and does not make reference to or allow conclusions about anyother hypotheses, such as the alternative hypothesis in Neyman- Pearson statistical hypothesis testing.
The conventional frequentist statistical hypothesis testing procedure is to formulate a research hypothesis, such as"people in higher social classes live longer", then collect relevant data, followed by carrying out a statistical significance test to see how likely such results would be found if chance alone were at work.
If X{\displaystyle X} is a random variable representingthe observed data and H{\displaystyle H} is the statistical hypothesis under consideration, then the notion of statistical significance can be naively quantified by the conditional probability Pr( A| H){\displaystyle\Pr(A|H)}, which gives the likelihood of a certain observation event A if the hypothesis is assumed to be correct.
This is further compounded by definitions of equivalence or statistical hypotheses that allow greater differences between treatment effects.
Estimating parameters Testing statistical hypotheses Providing a range of values instead of a point estimate Regression analysis.
On the basis of the comparison of φ* empirical and critical values,we conclude that one of the accepted statistical hypotheses is consistent.
The critical significance value accepted in this research as"р" is surely specified(e.g.,"Thecritical significance value when checking statistical hypotheses in this research was accepted equal 0.05").
Scientific hypotheses seek to provide an explanation for natural phenomena, whereas statistical hypotheses are generally used to establish the existence of relationships(or lack thereof) between data sets.
This type of hypothesis testing requires complex statistical tools.
It is also important to consider the statistical power of a hypothesis test when interpreting its results.
On the basis of the proposed measure of proximity, we construct a statistical test for testing the hypothesis on the equality of hypothetical distribution functions.
So in this next experiment, we're going to give babies just a tiny bit of statistical data supporting one hypothesis over the other, and we're going to see if babies can use that to make different decisions about what to do.
If we state one hypothesis only and the aim of the statistical test is to verify whether this hypothesis is not false, but not, at the same time, to investigate other hypotheses, then such a test is called a significance test.
Methods of verifying statistical hypotheses are called statistical tests.
On the Problem of the Most Efficient Tests of Statistical Hypotheses(coauthor Jerzy Neyman, 1933) The Application of Statistical Methods to Industrial Standardisation and Quality Control.