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In a hypothesis testing the alternative hypothesis is assumed?
No. The null hypothesis is assumed to be correct unless there is sufficient evidence from the sample and the given criteria (significance level) to reject it.
The closer the sample mean is to the population mean?
Your question is a bit difficult to understand. I will rephrase: In hypothesis testing, when the sample mean is close to the assumed mean of the population (null hypotheses), what does that tell you? Answer: For a given sample size n and an alpha value, the closer the calculated mean is to the assumed mean of the population, the higher chance that null hypothesis will not be rejected in favor of the alternative hypothesis.
Explain the differences between a sample and a population?
A sample consists of a small portion of data when a population is taken from a large amount.
Relationship between level of significance and hypothesis testing?
A physician wishes to study the relationship between hypertension and smoking habits. From a random sample of 180 individuals, the following results were obtainedAt the 5% level of significance, test whether the absence of hypertension is independent of smoking habits.HypertensionSmoking habitNon-smokersModerate smokersHeavy smokersYes213630No482619
What happens if a hypothesis is tested and shown to be false?
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.
