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What else can I help you with?
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.
Testing of Hypothesis?
A hypothesis is a proposed explanation which scientists test with the available scientific theories. There are four steps to testing a hypothesis; state the hypothesis, formulate an analysis plan, analyze sample data and interpret the results.
Why is important that a sample be random and representative when conducting hypothesis testing?
It helps you nawser
What role does Random Sampling Distribution in hypothesis testing?
It is an assumption to hypothesis testing. I can not comment on the significance of a violation of these assumptions without knowing how the non-random sample was taken.
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.
What is each sample does not necessarily have the same properties as every other sample?
You will typically have an experimental parameter that will be varied as part of testing a hypothesis.
When testing for differences between two means the sample population are?
You are testing the difference between two means of independent sample and the population variance are not known. from those population you take two samples of two different size n1and n2. what degrees of freedom is appropriate to consider in this case
What kind of decisions does hypothesis testing help us make?
Hypothesis testing helps us make decisions about the validity of a claim or hypothesis based on statistical evidence. By comparing observed data against a null hypothesis, we can determine whether to reject or fail to reject that hypothesis. This process aids in making informed conclusions about relationships or differences within data, guiding decisions in fields like science, business, and healthcare. Ultimately, it allows us to quantify uncertainty and assess the likelihood of outcomes based on sample data.
What is the t test?
The t-test is a statistical method used to determine if there is a significant difference between the means of two groups. It is commonly applied in hypothesis testing to compare sample data against a population or between two sample groups. The t-test accounts for variability and sample size, allowing researchers to infer whether observed differences are likely due to chance. There are different types of t-tests, including independent, paired, and one-sample t-tests, each suited for specific study designs.
The sum of the differences between sample observations and sample means is what?
always zero
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.
What is the nature of hypothesis testing?
A hypothesis must be subjected to rigorous testing before it becomes a theory. A hypothesis is used to explain some phenomenon about the natural world. Once a hypothesis has been created, it can be used to formulate predictions. These predictions in turn are then tested to be accurate through experimentation or observation.
What is the purpose of hypothesis testing?
The purpose of hypothesis testing is to determine whether there is enough statistical evidence in a sample of data to support or reject a specific claim about a population parameter. It involves formulating a null hypothesis (which represents no effect or no difference) and an alternative hypothesis (which represents an effect or difference), then using sample data to assess the likelihood of observing the data if the null hypothesis were true. By calculating a p-value and comparing it to a predetermined significance level, researchers can make informed decisions regarding the validity of the hypotheses. Ultimately, hypothesis testing aids in drawing conclusions from data and making informed decisions based on statistical evidence.
