What is meant by level of significance to retain or to reject a null hypothesis?
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Contents: Watch the video for an example:
Support or Reject a Null Hypothesis P Value Method Can’t see the video? Click here. In many statistical tests, you’ll want to either reject or support the null hypothesis. For elementary statistics students, the term can be a tricky term to grasp, partly because the name “null hypothesis” doesn’t make it clear about what the null hypothesis actually is! OverviewThe null hypothesis can be thought of as a nullifiable hypothesis. That means you can nullify it, or reject it. What happens if you reject the null hypothesis? It gets replaced with the alternate hypothesis, which is what you think might actually be true about a situation. For example, let’s say you think that a certain drug might be responsible for a spate of recent heart attacks. The drug company thinks the drug is safe. The null hypothesis is always the accepted hypothesis; in this example, the drug is on the market, people are using it, and it’s generally accepted to be safe. Therefore, the null hypothesis is that the drug is safe. The alternate hypothesis — the one you want to replace the null hypothesis, is that the drug isn’t safe. Rejecting the null hypothesis in this case means that you will have to prove that the drug is not safe. Vioxx was pulled from the market after it was linked to heart problems. To reject the null hypothesis, perform the following steps:Step 1: State the null hypothesis. When you state the null hypothesis, you also have to state the alternate hypothesis. Sometimes it is easier to state the alternate hypothesis first, because that’s the researcher’s thoughts about the experiment. How to state the null hypothesis (opens in a new window). Step 2: Support or reject the null hypothesis. Several methods exist, depending on what kind of sample data you have. For example, you can use the P-value method. For a rundown on all methods, see: Support or reject the null hypothesis. If you are able to reject the null hypothesis in Step 2, you can replace it with the alternate hypothesis. That’s it! When to Reject the Null hypothesisBasically, you reject the null hypothesis when your test value falls into the rejection region. There are four main ways you’ll compute test values and either support or reject your null hypothesis. Which method you choose depends mainly on if you have a proportion or a p-value.  Click the link the skip to the situation you need to support or reject null hypothesis for: Support or Reject Null Hypothesis with a P ValueIf you have a P-value, or are asked to find a p-value, follow these instructions to support or reject the null hypothesis. This method works if you are given an alpha level and if you are not given an alpha level. If you are given a confidence level, just subtract from 1 to get the alpha level. See: How to calculate an alpha level. Step 1:
State the null hypothesis and the alternate hypothesis (“the claim”). Step 2: Find the critical value. We’re dealing with a normally distributed population, so the critical value is a
z-score. Click here if you want easy, step-by-step instructions for solving this formula. Step 4: Find the P-Value by looking up your answer from step 3 in the z-table. To get the p-value, subtract the area from 1. For example, if your area is .990 then your p-value is 1-.9950 = 0.005. Note: for a two-tailed test, you’ll need to halve this amount to get the p-value in one tail. Step 5: Compare your answer from
step 4 with the α value given in the question. Should you support or reject the null hypothesis? P-Value GuidelinesUse these general guidelines to decide if you should reject or keep the null: If p value > .10 → “not
significant” That’s it! Back to Top Support or Reject Null Hypothesis for a ProportionSometimes, you’ll be given a proportion of the population or a percentage and asked to support or reject null hypothesis. In this case you can’t compute a test value by calculating a z-score (you need actual numbers for that), so we use a slightly different technique. Watch the video for an example: Hypothesis Test for a Proportion Can’t see the video? Click here. Example question: A researcher claims that Democrats will win the next election. 4300 voters were polled; 2200 said they would vote Democrat. Decide if you should support or reject null hypothesis. Is there enough evidence at α=0.05 to support this claim? Step
1: State the null hypothesis and the alternate hypothesis (“the claim”). Step 2: Compute
by dividing the number of positive respondents from the number in the random sample: Step 3: Use the following formula to calculate your test value. Where: The z-score is: Step 4: Look up Step 3 in the z-table to get .9418. Step 5: Calculate your
p-value by subtracting Step 4 from 1. Step 6: Compare your answer from step 5 with the α value given in the question. Support or reject the null hypothesis? If step 5 is less than α, reject the null hypothesis, otherwise do not reject it. In this case, .582 (5.82%) is not less than our α, so we do not reject the null hypothesis. Back to Top Support or Reject Null Hypothesis for a Proportion: Second exampleExample question: A researcher claims that more than 23% of community members go to church regularly. In a recent survey, 126 out of 420 people stated they went to church regularly. Is there enough evidence at α = 0.05 to support this claim? Use the P-Value method to support or reject null hypothesis. Step 1: State the null hypothesis and the alternate hypothesis (“the claim”). Ho:p ≤ 0.23; H1:p > 0.23 (claim) Step 2: Compute by dividing the number of positive respondents from the number in the random sample: Step 3: Find ‘p’ by converting the stated claim to a decimal: Step 4: Use the following formula to calculate your test value. Click here if you want easy, step-by-step instructions for solving this formula. If formulas confuse you, this is asking you to:
Step 5: Find the P-Value by looking up your answer from step 5 in the z-table.The z-score for 3.41 is .4997. Subtract from 0.500: 0.500-.4997 = 0.003. Step
6: Compare your P-value to α. Support or reject null hypothesis? If the P-value is less, reject the null hypothesis. If the P-value is more, keep the null hypothesis. Note: In Step 5, I’m using the z-table on this site to solve this problem. Most textbooks have the right of z-table. If you’re seeing .9997 as an answer in your textbook table, then your textbook has a “whole z” table, in which case don’t subtract from .5, subtract from 1. 1-.9997 = 0.003. Back to Top Check out our Youtube channel for video tips! ReferencesEveritt, B. S.; Skrondal, A. (2010), The Cambridge Dictionary
of Statistics, Cambridge University Press. ---------------------------------------------------------------------------
Need help with a homework or test question? With Chegg Study, you can get step-by-step solutions to your questions from an expert in the field. Your first 30 minutes with a Chegg tutor is free! Comments? Need to post a correction? Please Contact Us. What is the level of significance to reject a null hypothesis?A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random). Therefore, we reject the null hypothesis, and accept the alternative hypothesis.
How do you know when to accept or reject the null hypothesis?We assume that the null hypothesis is correct until we have enough evidence to suggest otherwise. After you perform a hypothesis test, there are only two possible outcomes. When your p-value is less than or equal to your significance level, you reject the null hypothesis. The data favors the alternative hypothesis.
What is the meaning of level of significance?Definition of level of significance
: the probability of rejecting the null hypothesis in a statistical test when it is true. — called also significance level.
What does level of significance mean in hypothesis testing?Significance Levels. The significance level for a given hypothesis test is a value for which a P-value less than or equal to is considered statistically significant. Typical values for are 0.1, 0.05, and 0.01. These values correspond to the probability of observing such an extreme value by chance.
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