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Dear student we can provide you with the solution of one type of question. These are 3 seperate questions  1)  A hypothesis is an informed supposition about something in your general surroundings. It ought to be testable, either by trial or observation. For instance:  Another medication you think may work.  A method of training you think maybe better. If you are going to propose a hypothesis, it’s a must that you write a statement. Hypothesis testing in statistics is a path for you to test the results of a survey or experiment to check whether you have significant results. You're fundamentally testing whether your results are substantial by sorting out the odds that your results have occurred by chance. In the event that your results may have occurred by chance, the experiment will not be repeatable thus has little use.  Basic components are  Null hypothesis: Null hypothesis is a factual hypothesis that expects that the observation is because of a chance factor. Null hypothesis is signified by; H0: μ1 = μ2, which shows that there is no distinction between the two population means.  Elective hypothesis: Contrary to the null hypothesis, the elective hypothesis shows that observations are the aftereffect of a genuine impact.  Level of importance: Refers to the level of importance in which we acknowledge or reject the null-hypothesis. 100% exactness isn't workable for tolerating or rejecting a hypothesis, so we thusly select a degree of importance that is typically 5%.  Type I error: When we reject the null hypothesis, albeit that hypothesis was valid. Type I error is signified by alpha. In hypothesis testing, the normal curve that shows the basic region is known as the alpha region.  Type II errors: When we acknowledge the null hypothesis yet it is bogus. Type II errors are meant by beta. In Hypothesis testing, the normal curve that shows the acknowledgment region is known as the beta region.  Power: Usually known as the likelihood of accurately tolerating the null hypothesis. 1-beta is called power of the analysis.  One-tailed test: When the given factual hypothesis is one worth like H0: μ1 = μ2, it is known as the one-tailed test.  Two-tailed test: When the given statistics hypothesis accepts a not exactly or more prominent than esteem, it is known as the two-tailed test. ...