That there is no difference between the means for the two populations.
The hypothesis in research is an idea or concept that may be true. Through proper experimentation, a hypothesis can become a fact. So, in research, you test a hypothesis to see if it is true. You will see that the null hypothesis, from the related link, is what you are testing against. Perhaps you have a new medicine, and you want to know if it improves the health of a patient. Your null hypothesis is that this treatment does not cause any improvement.
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.
Identification of a null and alternative hypothesis is used in statistical hypothesis testing. I've included two links which will show you how to formulate these hypothesis. The source of the null hypothesis is considered the "status quo" or what would be assumed without data. Perhaps, you buy 20 light bulbs which average 2500 hours of service, and 14 of these burn out after 2400 hours of service. Your null hypothesis is that the light bulbs will last 2500 hours or more, while the null hypothesis is that they will burn out on the average in less than 2500 hours. Hypothesis in application of the scientific method is entirely another matter. Please see related links.
You can calculate a result that is somehow related to the mean, based on the data available. Provided that you can work out its distribution under the null hypothesis against appropriate alternatives, you have a test statistic.
The p value for rejecting an hypothesis is more closely related to the type of errors and their consequences. The p value is not determined by the chi square - or any other - test but by the impact of the decision made on the basis of the test. The two types of errors to be considered are: what is the probability that you reject the null hypothesis when it is actually true (type I error), and what is the probability that you accept the null hypothesis when, in fact, it is false (type I error).. Reducing one type of error increase the other and there is a balance to be struck between the two. This balance will be influenced by the costs associated with making the wrong error. In real life, the effects (costs/benefits) of decisions are very asymmetrical.
The sex hypothesis suggests that differences in behavior between males and females are influenced by evolutionary factors related to reproduction. This hypothesis impacts our understanding of human behavior by highlighting the role of biological differences in shaping behaviors such as mate selection, aggression, and parenting strategies.
See related for the differences.
The relationship between theories, concepts, and hypothesis is that a theory is a model of how concepts are related, the concepts are categorical ideas that are represented by our variables and hypothesis are predictions of how concepts are related, often deduced from a theory.
See the graph in the related link. It clearly separates out the differences between PDLC and SDLC.
The hypothesis and the Data are related because the hypothesis is what you think is going to happen, and if you're right, then that becomes the data
its easy hypothesis is the conclusion of the answers
Sturtevant's hypothesis was that the frequency of cross-overs during meiosis was related to the distance between genes
The digital divide refers to the gap between those who have access to technology and those who do not. The knowledge-gap hypothesis suggests that disparities in information flow through media contribute to existing social inequalities. In the context of digital technology, the digital divide can exacerbate knowledge gaps by limiting access to information and resources for marginalized populations.
Eagles are much larger..They are closely related.
A hypothesis and predicition r kinda alike and the experiment is the testing of the hypothesis and prediction
See the related links below for websites that feature pictures depicting the differences between male and female genitalia. See the related question below for more info.
A hypothesis is what you believe will happen when you do an experiment. Scientific theory is when you use the data you have received from an experiment and create an idea that best suits your results. A theory can be related back to your original hypothesis, the experiment can prove whether your hypothesis was right.