The conditions of what is called his control group.
In Karl Popper's terminology there must be a way to prove a hypothesis false. That is what it means when scientists say that a specific hypothesis is a "testable hypothesis".
Hypothesis testing is how science progresses. Scientists come up with hypotheses (pl. for hypothesis) based on their observations and what is Known to be true (i.e. Laws of Physics...like gravity). Hypotheses are designed to answer a question and, by definition, MUST be DISprovable. If a hypothesis cannot be disproven, it has no value. For example, if I look outside and then say "I see the sky is blue"...that is not a hypothesis, it is an observation (or maybe a fact...albeit an optical illusion). However, if I look out the window and say "the sky ALWAYS appears blue"....That is a hypothesis, because it is testable and can be disproven, since tomorrow it might be gray.
a best educated guess
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.
a reveiw of what is known about the subject must occur
Scientists repeat experiments for reliability. The experiment must be repeated for the scientist to develop a theory. One experiment does not prove your hypothesis correct; therefore, it must be done a several times.
Unlike a wild guess, a hypothesis is based on observations and it must be testable......:-) answer by hismejohn
To formulate a hypothesis effectively using hypothesis testing, one must first identify a research question and make a clear statement about the relationship between variables. Then, the hypothesis should be specific, testable, and based on existing knowledge or theory. Finally, the hypothesis should be framed in a way that allows for statistical analysis to determine its validity.
Scientists repeat experiments for reliability. The experiment must be repeated for the scientist to develop a theory. One experiment does not prove your hypothesis correct; therefore, it must be done a several times.
A stable scientific hypothesis allows for consistency and reliability in testing and results. It enables the hypothesis to be accurately evaluated and potentially confirmed or refuted through experimentation. Stability ensures that the hypothesis accurately reflects the phenomena being studied.
The difference between the null hypothesis and the alternative hypothesis are on the sense of the tests. In statistical inference, the null hypothesis should be in a positive sense such in a sense, you are testing a hypothesis you are probably sure of. In other words, the null hypothesis must be the hypothesis you are almost sure of. Just an important note, that when you are doing a tests, you are testing if a certain event probably occurs at certain level of significance. The alternative hypothesis is the opposite one.
A scientific explanation is a hypothesis derived from existing research or observations that can be tested through experiments or further observation. It must be based on empirical evidence and subject to scrutiny and validation by the scientific community. By testing the explanation, scientists can determine its validity and refine our understanding of the natural world.