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Course: Statistics and probability>Unit 12

Lesson 2: Error probabilities and power

Examples identifying Type I and Type II errors

Examples identifying Type I and Type II errors.

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• I thought null hypotheses only have statements of equality, wouldn't statement of equality be enough for this example ?
• In example 2, if `p` is less than 0.40, you would still not want to build the cafeteria. After all, it could be the case that 30% or 10% or even 0% of the people are interested in the meal plan. If you were to set `H_0: p = 0.40`, then you would ignore all these less than options, so we need the less than or equal sign.
• An interesting example this is. Suppose that building a cafeteria entails profits if more that 40 percent of the students make a purchase (interested = would purchase) a meal plan. Which is more serious
1) Type 1 error: lose the opportunity to make profits?
2) Type 2 error: bear the cost and the loss if a cafetria is built?
(1 vote)
• Apart from decreasing alpha how else can I lower the chances of making a type 1 error?
(1 vote)
• Asking for a communication research methods class:

I was told the null hypothesis is always a statement of "no difference" in my statistics class? Is this true? In this example it seems as its phrased in a more complicated manner. But essentially the equal sign is still saying, there is no difference, right? I'm confused by how this question is framed.
(1 vote)
• Yes, that is correct. The wording can be confusing, but the null hypothesis is just stating the assumed fact.
(1 vote)
• I read that statistical significance means that the result didn't happen by chance, isn't random, therefore something else caused it (not the population we already have). Is this correct? If a p-value is statistically significant, does it mean it is smaller than alpha and we reject the null hypothesis?