Hypothesis Testing (Conceptual Problem)

In summary, the conversation is discussing whether the reasons of 1) the value of ##\mu_0## not being in the confidence interval and 2) the p-value being less than alpha are sufficient to reject the null hypothesis in a given scenario. The speaker also asks for suggestions and comments on this matter.
  • #1
icystrike
445
1

Homework Statement


This is not a homework problem, rather it is a conceptual question.

I am wondering if the below is an accurate illustration to explain why I reject the null hypothesis.
Such that
[itex] H_{0}: \mu=\mu_{0} [/itex]
[itex] H_{a}: \mu\ne\mu_{0} [/itex]

By relating the reasoning with the diagram attached.
1) [itex] \mu_{0} [/itex]is not in the confidence interval
2) [itex] p-value < \alpha [/itex]

Please suggest and comments :)
 

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  • #2
icystrike said:

Homework Statement


This is not a homework problem, rather it is a conceptual question.

I am wondering if the below is an accurate illustration to explain why I reject the null hypothesis.
Such that
[itex] H_{0}: \mu=\mu_{0} [/itex]
[itex] H_{a}: \mu\ne\mu_{0} [/itex]

By relating the reasoning with the diagram attached.
1) [itex] \mu_{0} [/itex]is not in the confidence interval
2) [itex] p-value < \alpha [/itex]

Please suggest and comments :)
What exactly are you asking? As I understand it, you are asking if those two reasons are adequate to reject ##H_0##, in which case, it almost follows directly from the definition of the ideas of confidence intervals and significance tests that they are.
I don't think you are asking that. Would you please clarify what you are asking?
 

Related to Hypothesis Testing (Conceptual Problem)

What is a hypothesis?

A hypothesis is a proposed explanation or prediction for a phenomenon or observation. It is a statement that can be tested and either supported or rejected based on evidence.

Why do we use hypothesis testing?

Hypothesis testing is used to determine the validity of a hypothesis and to make decisions based on statistical evidence. It helps to determine whether the observed data supports or refutes the proposed hypothesis.

What is the process of hypothesis testing?

The process of hypothesis testing involves formulating a null hypothesis and an alternative hypothesis, collecting and analyzing data, and using statistical tests to determine the likelihood of the observed data if the null hypothesis were true. Based on the results, the null hypothesis may be rejected or failed to be rejected.

What is a Type I error and a Type II error?

A Type I error occurs when the null hypothesis is rejected when it is actually true. This means that the observed data is unlikely to have occurred by chance and the decision to reject the null hypothesis is incorrect. A Type II error occurs when the null hypothesis is failed to be rejected when it is actually false. This means that the observed data is likely to have occurred by chance and the decision to not reject the null hypothesis is incorrect.

What are some common mistakes to avoid in hypothesis testing?

Some common mistakes in hypothesis testing include using an incorrect statistical test, failing to account for confounding variables, and drawing conclusions based on results without considering the limitations of the study. It is important to carefully plan and design the study, accurately collect and analyze data, and consider alternative explanations for the results.

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