If my p-value = .322 and my significant level is .05 , then

My Null Hypothesis is rejected and I must accept the Alternative hypothesis

My Null Hypothesis is not rejected

My Alternative hypothesis becomes the new Null Hypothesis

I cannot make a decision until I know my sample size.

If significance level = .05, then you will reject the null hypothesis, if P ≤ .05. Does that help?

My significant value is .322 then hypothesis will be rejected or accepted

Well, statistically speaking, if your p-value is larger than your significance level, which is 0.05 in this case, you would fail to reject the null hypothesis and it stays intact. So, the correct answer would be: "My null hypothesis is not rejected." But hey, don't be too hard on the null hypothesis, it's just trying to fit in!

Based on the given information, if the p-value (probability value) is 0.322 and the significance level (alpha) is 0.05, then the correct answer is:

My Null Hypothesis is not rejected.

The p-value measures the strength of evidence against the null hypothesis. In this case, since the p-value (0.322) is greater than the significance level (0.05), it means that there is not enough evidence to reject the null hypothesis. The alternative hypothesis is not accepted, and the original null hypothesis remains in place.

To answer this question, you need to understand the concept of hypothesis testing and the role of the p-value and significance level.

Hypothesis testing is a statistical method used to make inferences about a population based on a sample of data. In hypothesis testing, we start with two competing hypotheses: the null hypothesis (H0) and the alternative hypothesis (Ha).

The null hypothesis (H0) is the default assumption that there is no significant difference or effect in the population, while the alternative hypothesis (Ha) is the claim that contradicts the null hypothesis and suggests that there is a significant difference or effect in the population.

The p-value is a measure of the evidence against the null hypothesis. It represents the probability of obtaining a test statistic as extreme as (or more extreme than) the one observed, assuming the null hypothesis is true. A small p-value indicates strong evidence against the null hypothesis, while a large p-value suggests weak evidence against the null hypothesis.

The significance level (or alpha), often denoted as α, is the maximum probability of rejecting the null hypothesis when it is true. It is set prior to conducting the hypothesis test and is typically chosen as 0.05 or 0.01.

Now, let's apply this knowledge to answer the question.

If the p-value is 0.322 and the significance level is 0.05, we compare the p-value and the significance level to make a decision.

If the p-value is greater than the significance level (p-value > α), we fail to reject the null hypothesis. This means that there is not enough evidence to suggest that the alternative hypothesis is true. Therefore, the correct statement in this case is "My Null Hypothesis is not rejected."

If the p-value is less than or equal to the significance level (p-value ≤ α), we reject the null hypothesis in favor of the alternative hypothesis. This means that there is enough evidence to suggest that the alternative hypothesis is true. In this case, none of the given options are correct.

In conclusion, based on the given information, the correct statement is "My Null Hypothesis is not rejected."