In statistical hypothesis testing you have a null hypothesis against which you are testing an alternative. The hypothesis concerns one or more characteristics of the distribution. It is easier to illustrate the idea of directional and non-directional hypothesis. In studying the academic abilities of boys and girls the null hypothesis would be that boys and girls are equally able. One directional hypothesis would be that boys are more able. The non-directional alternative would be that there is a gender difference. You have no idea whether boys are more able or girls - only that they are not the same.
no,these are not the same thing.The values at each end of the interval are called the confidence limits.
No
The relationship between algebra and statistics may not be immediately apparent. In algebra, you learn how to change an expression from y equals a function of x to x equals a function of y. This ability to transform equations by the rules of algebra is very important in statistics. The standard textbooks in statistics provide equations identifying how to calculate the mean and standard deviation. Generally, from this point, the ability of these statistics based on a limited sample size, to infer (or suggest) properties of the population is introduced. The rules of algebra are used to transform the equation which provides confidence intervals given a sample size to one that provides the sample size given a confidence interval. Similarly, in hypothesis testing, algebra is used again. I can be given a certain level of significance, and decide whether to accept (fail to reject) or reject the null hypothesis. Or, the same equations can be transformed to identify what level of significance is needed to accept the null hypothesis. Algebra is required to understand the relationships between equations. You can think of statistic equations of a series of building blocks, and with algebra you can understand how one equation is derived from another. Not only algebra, but many other areas of mathematics (geometry, trigonometry and calculus) are used in statistics.
Stochastic testing is the same as "monkey testing", but stochastic testing is a lot more technical sounding name for the same testing process. Stochastic testing is black box testing, random testing, performed by automated testing tools. Stochastic testing is a series of random tests over time. The software under test typically passes the individual tests, but our goal is to see if it can pass a large number of individual tests.
Yes, it can.
In statistical hypothesis testing you have a null hypothesis against which you are testing an alternative. The hypothesis concerns one or more characteristics of the distribution. It is easier to illustrate the idea of directional and non-directional hypothesis. In studying the academic abilities of boys and girls the null hypothesis would be that boys and girls are equally able. One directional hypothesis would be that boys are more able. The non-directional alternative would be that there is a gender difference. You have no idea whether boys are more able or girls - only that they are not the same.
We could test our hypothesis by means of experimentation, Sorry if you didn't had the information you needed. I didn't understand your question.
You will typically have an experimental parameter that will be varied as part of testing a hypothesis.
The Confidence Interval is a particular type of measurement that estimates a population's parameter. Usually, a confidence interval correlates with a percentage. The certain percentage represents how many of the same type of sample will include the true mean. Therefore, we would be a certain percent confident that the interval contains the true mean.
Every hypothesis is a proposed explanation for a phenomenon that can be tested through experimentation and observation. Additionally, every hypothesis should be falsifiable, meaning it can be proven wrong through testing. Finally, a hypothesis should be specific and make a clear prediction that can be tested.
We do not make a clear separation between "proven true" and "proven false" in hypothesis testing. Hypothesis testing in statistical analysis is used to help to make conclusions based on collected data. We always have two hypothesis and must chose between them. The first step is to decide on the null and alternative hypothesis. We also must provide an alpha value, also called a level of significance. Our null hypothesis, or status quo hypothesis is what we might conclude without any data. For example, we believe that Coke and Pepsi tastes the same. Then we do a survey, and many more people prefer Pepsi. So our alternative hypothesis is people prefer Pepsi over Coke. But our sample size is very small, so we are concerned about being wrong. From our data and level of significance, we find that we can not reject the null hypothesis, so we must conclude that Coke and Pepsi taste the same. The options in hypothesis testing are: Null hypothesis rejected, so we accept the alternative or Null hypothesis not rejected, so we accept the null hypothesis. In the taste test, we could always do a larger survey to see if the results change. Please see related links.
no,these are not the same thing.The values at each end of the interval are called the confidence limits.
no its not
At the same level of significance and against the same alternative hypothesis, the two tests are equivalent.
No, a hypothesis is a proposed explanation or prediction that is based on limited evidence. It is subject to testing and may be supported or refuted by empirical data. Only after rigorous testing and analysis can a hypothesis be confirmed as a theory or scientific fact.
Exactly the same