• semidevil
In summary, a hypothesis test is a statistical method used to determine whether a hypothesis about a population is supported by the data. The hypothesis should be based on a research question, be testable, specific, and supported by existing knowledge. There is a difference between a null and alternative hypothesis, with the null hypothesis assuming no significant relationship between variables. The results of a hypothesis test can be interpreted using a p-value, and limitations include the inability to prove causation and the influence of various factors on the results.
semidevil
i'm getting my concepts very confused now...

so the formula for the normal distri is (y - u)/(sigma)/((sqroot(n)).

so if I want to test y, I set that formula = to alpha(where alpha is the confidence level).

that is if it is one sided. if it is 2 sided, do I still set it to alpha, or alpha/2?

i'm pretty sure if it's 2 sided then you set it to $$\frac{\alpha}{2}$$

It is understandable to feel confused about hypothesis testing, as it can be a complex topic. However, it is important to clarify some concepts to better understand the formula and how to use it in hypothesis testing.

Firstly, the formula you mentioned is the formula for the z-test, which is used to test hypotheses involving a population mean when the population standard deviation is known. This formula is used to calculate the z-score, which is then compared to a critical value to determine the statistical significance of the results.

Secondly, when conducting a one-sided test, the alpha level is typically set to the desired level of significance (e.g. 0.05 or 0.01). This means that the calculated z-score needs to be equal to or greater than the critical value in order to reject the null hypothesis. However, for a two-sided test, the alpha level is divided by 2 and used for both tails of the distribution. This is because a two-sided test is concerned with the possibility of a significant difference in either direction, so we need to account for both tails of the distribution.

In summary, for a one-sided test, the formula is set equal to the alpha level, and for a two-sided test, the formula is set equal to alpha/2. I hope this helps clarify your confusion about hypothesis testing. Remember to always carefully consider the type of test you are conducting and the appropriate alpha level to use.

## 1. What is a hypothesis test?

A hypothesis test is a statistical method used to determine whether a hypothesis about a population is supported by the data. It involves formulating a null hypothesis and an alternative hypothesis, collecting data, and using statistical analysis to determine the likelihood of the null hypothesis being true.

## 2. How do you choose a hypothesis?

The hypothesis should be based on a research question and should be testable. It should also be specific, clearly stating the relationship between variables. Additionally, the hypothesis should be based on existing knowledge and theories, and should be supported by evidence or prior research.

## 3. What is the difference between a null and an alternative hypothesis?

A null hypothesis states that there is no significant difference or relationship between variables, while an alternative hypothesis states that there is a significant difference or relationship. In hypothesis testing, the null hypothesis is assumed to be true until there is sufficient evidence to reject it in favor of the alternative hypothesis.

## 4. How do you interpret the results of a hypothesis test?

The results of a hypothesis test provide a p-value, which is the probability of obtaining the observed data if the null hypothesis is true. If the p-value is less than the chosen significance level (usually 0.05), then the null hypothesis is rejected in favor of the alternative hypothesis. This means that there is sufficient evidence to support the alternative hypothesis.

## 5. What are the limitations of hypothesis testing?

One of the limitations of hypothesis testing is that it only provides evidence for or against the null hypothesis, and does not prove causation. Additionally, the results of a hypothesis test may be influenced by factors such as sample size, data collection methods, and assumptions made in the statistical analysis. It is important to consider these limitations when interpreting the results of a hypothesis test.

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