And given that the null hypothesis is true, we say OK, if the null hypothesis is true then the mean is usually going to be equal to some value. So we create some distribution. Assuming that the null hypothesis is true, it normally has some mean value right over there.
Then we have some statistic and we're seeing if the null hypothesis is true, what is the probability of getting that statistic, or getting a result that extreme or more extreme then that statistic. And then if that's low enough of a threshold for us, we will reject the null hypothesis. So in this case we will-- so actually let's think of it this way.
We say look, we're going to assume that the null hypothesis is true. So let's say we're looking at sample means. We get a sample mean that is way out here. So we are going to reject the null hypothesis. So we will reject the null hypothesis. Now what does that mean though?
Let's say that this area, the probability of getting a result like that or that much more extreme is just this area right here. For example, a p -value of 0. However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists thus risking a type II error.
A type II error is also known as a false negative and occurs when a researcher fails to reject a null hypothesis which is really false.
Here a researcher concludes there is not a significant effect, when actually there really is. You can decrease your risk of committing a type II error by ensuring your test has enough power. You can do this by ensuring your sample size is large enough to detect a practical difference when one truly exists. The consequences of making a type I error mean that changes or interventions are made which are unnecessary, and thus waste time, resources, etc. Type II errors typically lead to the preservation of the status quo i.
To indirectly reduce the risk of a Type II error, you can increase the sample size or the significance level to increase statistical power. Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test.
Significance is usually denoted by a p -value , or probability value. Statistical significance is arbitrary — it depends on the threshold, or alpha value, chosen by the researcher. When the p -value falls below the chosen alpha value, then we say the result of the test is statistically significant. In statistics, power refers to the likelihood of a hypothesis test detecting a true effect if there is one.
A statistically powerful test is more likely to reject a false negative a Type II error. Your study might not have the ability to answer your research question. Have a language expert improve your writing. Check your paper for plagiarism in 10 minutes.
Do the check. Generate your APA citations for free! APA Citation Generator. What can proofreading do for your paper? How do you reduce the risk of making a Type I error? To reduce the Type I error probability, you can set a lower significance level. How do you reduce the risk of making a Type II error? What is statistical significance? What is statistical power? Is this article helpful? Pritha Bhandari Pritha has an academic background in English, psychology and cognitive neuroscience.
As an interdisciplinary researcher, she enjoys writing articles explaining tricky research concepts for students and academics. Other students also liked. An introduction to statistical significance If a result is statistically significant, that means it's unlikely to be explained solely by random factors or chance.
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