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ur new spam email filter uses this test in "real" emails going to your spam send spam emails to your inbox. Since your inbox, you don't really care that want to be constantly checking your positives. Assuming your software It would be one way to reduce the false 10 a parametric approach

ur new spam email filter uses this test in "real" emails going to your spam send spam emails to your inbox. Since your inbox, you don't really care that want to be constantly checking your positives. Assuming your software It would be one way to reduce the false 10 a parametric approach

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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In the figure below, the dotted vertical line indicates the upper 2.5% tail of the null distribution, while the solid vertical line indicates the lower 2.5% tail of the alternate distribution. The density of the null is shown in a dashed line, while the density of the alternate is shown in a solid line. Both the null and the alternate are normal distributions with equal variances. Imagine your new spam email filter uses this test statistic. False positives (or type I errors) result in "real" emails going to your spam folder, while false negatives (or type II errors) send spam emails to your inbox. Since it's not such a big deal to delete an email from your inbox, you don't really care that much about false negatives. But, as you don't want to be constantly checking your spam folder, you really want to minimize false positives. Assuming your software programmed a two-tailed hypothesis test, what would be one way to reduce the false positive error rate? LA 0 -5 test statistic 5 10 Decrease beta (false negatives) Use a non-parametric approach instead of a parametric approach Increase alpha from 5% to 10% or higher Increase the cumulative probability of the test statistic from 0.95 to 0.99 Switch to a one-sided lower-tail hypothesis test
Transcribed Image Text:In the figure below, the dotted vertical line indicates the upper 2.5% tail of the null distribution, while the solid vertical line indicates the lower 2.5% tail of the alternate distribution. The density of the null is shown in a dashed line, while the density of the alternate is shown in a solid line. Both the null and the alternate are normal distributions with equal variances. Imagine your new spam email filter uses this test statistic. False positives (or type I errors) result in "real" emails going to your spam folder, while false negatives (or type II errors) send spam emails to your inbox. Since it's not such a big deal to delete an email from your inbox, you don't really care that much about false negatives. But, as you don't want to be constantly checking your spam folder, you really want to minimize false positives. Assuming your software programmed a two-tailed hypothesis test, what would be one way to reduce the false positive error rate? LA 0 -5 test statistic 5 10 Decrease beta (false negatives) Use a non-parametric approach instead of a parametric approach Increase alpha from 5% to 10% or higher Increase the cumulative probability of the test statistic from 0.95 to 0.99 Switch to a one-sided lower-tail hypothesis test
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