A hypothesis is a suggestion of a way to explain something. If the hypothesis is tested and confirmed, it can advance to the status of theory. The conclusion of testing a hypothesis will be either that the hypothesis is confirmed, or it is not confirmed.
sorry
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.
examining/ experimenting/ testing/ verifying... it depends on the type of hypothesis to an extent I think.
It depends on the population.Use t-test for a small population, N < 30; otherwiase, apply z-test or when N>=30.
A hypothesis is a suggestion of a way to explain something. If the hypothesis is tested and confirmed, it can advance to the status of theory. The conclusion of testing a hypothesis will be either that the hypothesis is confirmed, or it is not confirmed.
A non-directional research hypothesis is a kind of hypothesis that is used in testing statistical significance. It states that there is no difference between variables.
Making an educated guess or prediction is more aligned with forming the hypothesis. This is an initial statement or proposal about what you expect to find or observe in a scientific investigation. Hypothesis testing involves conducting experiments or gathering data to evaluate the validity of the hypothesis.
That there is no difference between the means for the two populations.
A hypothesis is composed of two parts: the null hypothesis, which states that there is no effect or no difference between groups, and the alternative hypothesis, which states that there is an effect or a difference. These two components together form the basis for statistical testing and inference in research.
A hypothesis is a model or in other words a design of experiments to be tested with some theoretical basis and requires testing to verify the expected course
A hypothesis is a model or in other words a design of experiments to be tested with some theoretical basis and requires testing to verify the expected course
A hypothesis is an 'educated guess' based on observation and common sense. A theory is a commonly-accepted hypothesis that has held under the pressure of testing by many different scientists.
Describe the asymmetry between falsification and verification in the process of hypothesis testing
The answer is: a scientific method
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 hypothesis statement consists of three parts: the null hypothesis (H0), the alternative hypothesis (Ha), and the level of significance (alpha). The null hypothesis states that there is no relationship or difference between variables, while the alternative hypothesis suggests the presence of a relationship or difference. The level of significance determines the threshold for accepting or rejecting the null hypothesis based on statistical testing.