# Type 1 and type 2 errors?

Let's say I'm testing anti-spam software. The number of false positives (aka, friendly messages misidentified as spam, for those who don't know the term) is 40. The number of false negatives (spam messages misidentified as friendly) is also 40. I'm testing 100 messages. How many more messages would I need to test in order to be 99.99% that the null hypothesis can/cannot be rejected?

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EnumaElish
Homework Helper
Thanks for the link, but I'm not seeing any equation whatsoever that will help. As far as I can see, all the listed equations have to do with means.

EnumaElish
Homework Helper
Oh, so what's your null hypothesis? I thought you were to test that the average message is not spam.

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I just don't see how a sample mean would be relevant. Your understanding of the question is correct.

D H
Staff Emeritus

Also, you have told us about 80 messages, what about the other 20?

Also, you have told us about 80 messages, what about the other 20?
Let's say my null hypothesis is that a spam message will be correctly marked as spam. As for the 20 messages, lets say that those are false negatives.

D H
Staff Emeritus