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Experiments – Types of Errors
Review: What is a significance level?
What does significance level mean?
This is represented by alpha (α)
What is the significance level that we have been using?
New: What other value is often considered as an appropriate significance level?
Note:
Types of Errors that might occur when running a hypothesis test.
Type I:
Type II:
Important things to note:
Type I: This is more likely to happen when the significance level is higher (so the
lower the significance level, the lower the Type I error rate).
If the null hypothesis is false, then it is impossible to make a Type I error.
Technically: The significance level (α) is the probability of a Type I error
occurring given that the null hypothesis is true.
Type II: This is not really an error, since failing to reject the null hypothesis does
not mean we are saying that the null hypothesis is true. Instead we are
saying that we just don’t have enough information to conclude anything yet.
A Type II error can only occur if the null hypothesis is false.
To clarify, fill out the chart given.
EX: In the population, there is no difference between men and women on a certain test. However, you found a difference in your sample. There probability value for the data was .03, so you rejected the null hypothesis. What type of error did you make or is this the right decision?
EX: As the significance level gets lower, which error rate also gets lower?
In other words:
If we reject Ho when Ho is actually true, we have committed a Type ___ error.
If we fail to reject Ho when Ho is false, we have committed a Type ___ error.
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