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Christopher Dougherty EC220 - Introduction to econometrics (chapter 2) Slideshow: type 1 error and type 2 error Original citation: Dougherty, C. (2012) EC220 - Introduction to econometrics (chapter 2). [Teaching Resource] © 2012 The Author This version available at: Available in LSE Learning Resources Online: May 2012 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License. This license allows the user to remix, tweak, and build upon the work even for commercial purposes, as long as the user credits the author and licenses their new creations under the identical terms.

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22 2 +sd 2 -sd TYPE I ERROR AND TYPE II ERROR 5% level hypothetical distribution under acceptance region for b b2b2 sd sd 2.5% In the previous sequence a Type I error was defined to be the rejection of a null hypothesis when it happens to be true. 1

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22 2 +sd 2 -sd TYPE I ERROR AND TYPE II ERROR 5% level hypothetical distribution under acceptance region for b b2b2 sd sd 2.5% In hypothesis testing there is also a possibility of failing to reject the null hypothesis when it is in fact false. This is known as a Type II error. 2

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22 2 +sd 2 -sd TYPE I ERROR AND TYPE II ERROR 5% level hypothetical distribution under acceptance region for b b2b2 sd sd This sequence will demonstrate that there is a trade-off between the risk of making a Type I error and the risk of making a Type II error %

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22 2 +sd 2 -sd TYPE I ERROR AND TYPE II ERROR 5% level hypothetical distribution under acceptance region for b The diagram show the acceptance region and the rejection regions for a 5% significance test. The risk of making a Type I error, if the null hypothesis happens to be true, is 5%. 4 b2b2 sd sd 2.5%

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22 2 +sd sd 2 -sd sd 0.5% TYPE I ERROR AND TYPE II ERROR 5% level 1% level hypothetical distribution under acceptance region for b Using a 1% significance test, instead of a 5% test, reduces the risk of making a Type I error to 1%, if the null hypothesis is true. 5 b2b2

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22 2 +sd sd 2 -sd sd 0.5% TYPE I ERROR AND TYPE II ERROR 5% level 1% level hypothetical distribution under acceptance region for b We will consider the implications of the choice of significance test for the case where the null hypothesis happens to be false. 6 b2b2

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22 2 +sd sd 2 -sd sd 0.5% TYPE I ERROR AND TYPE II ERROR 7 5% level 1% level The diagram above explains how the test decisions are made, but it does not give the actual distribution of b 2. (For that reason the curve has been drawn with a dashed line.) b2b2 hypothetical distribution under acceptance region for b

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22 0.5% acceptance region for b 2 TYPE I ERROR AND TYPE II ERROR 5% level 1% level actual distribution under b2b2 2 +2sd 2 +sd 22 2 -sd 2 -2sd hypothetical distribution under Suppose that H 1 : 2 = 2 1 is in fact true and the distribution of b 2 is therefore governed by the right-hand curve. 8

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22 0.5% acceptance region for b 2 TYPE I ERROR AND TYPE II ERROR 5% level 1% level actual distribution under b2b2 2 +2sd 2 +sd 22 2 -sd 2 -2sd hypothetical distribution under If we obtain some data and run a regression, the estimate b 2 might be as shown. In this case we would make the right decision and reject H 0, no matter which test we used. 9

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22 0.5% acceptance region for b 2 TYPE I ERROR AND TYPE II ERROR 5% level 1% level actual distribution under b2b2 2 +2sd 2 +sd 22 2 -sd 2 -2sd hypothetical distribution under Here is another estimate. Again, we would make the right decision and reject the null hypothesis, no matter whether we use the 5% test or the 1% test. 10

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22 0.5% acceptance region for b 2 TYPE I ERROR AND TYPE II ERROR 5% level 1% level actual distribution under b2b2 2 +2sd 2 +sd 22 2 -sd 2 -2sd hypothetical distribution under In the case shown, we would make a Type II error and fail to reject the null hypothesis, using either significance level. 11

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22 0.5% acceptance region for b 2 TYPE I ERROR AND TYPE II ERROR 5% level 1% level actual distribution under b2b2 2 +2sd 2 +sd 22 2 -sd 2 -2sd hypothetical distribution under But in the case of this estimate, we would make the right decision if we used a 5% test but we would make a Type II error if we used a 1% test. 12

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22 0.5% acceptance region for b 2 TYPE I ERROR AND TYPE II ERROR 5% level 1% level actual distribution under b2b2 2 +2sd 2 +sd 22 2 -sd 2 -2sd hypothetical distribution under The probability of making a Type II error if we use a 1% test is given by the probability of b 2 lying within the 1% acceptance region, the interval between the red vertical dotted lines. 13

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22 0.5% acceptance region for b 2 TYPE I ERROR AND TYPE II ERROR 5% level 1% level actual distribution under b2b2 2 +2sd 2 +sd 22 2 -sd 2 -2sd hypothetical distribution under Given that H 1 is true, the probability of b 2 lying in the acceptance region is that area under the distribution for H 1 in the diagram - the pink shaded area in the diagram. 14

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22 0.5% acceptance region for b 2 TYPE I ERROR AND TYPE II ERROR 5% level 1% level actual distribution under b2b2 2 +2sd 2 +sd 22 2 -sd 2 -2sd If instead we use a 5% significance test, the probability of making a Type II error if H 1 is true is given by the area under the distribution for H 1 in the acceptance region for the 5% test. 15 hypothetical distribution under

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22 0.5% acceptance region for b 2 TYPE I ERROR AND TYPE II ERROR 16 5% level 1% level actual distribution under b2b2 2 +2sd 2 +sd 22 2 -sd 2 -2sd This is the gray shaded area in the diagram. In this particular case, using a 5% test instead of a 1% test would approximately halve the risk of making a Type II error. hypothetical distribution under

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22 0.5% acceptance region for b 2 TYPE I ERROR AND TYPE II ERROR 5% level 1% level actual distribution under b2b2 2 +2sd 2 +sd 22 2 -sd 2 -2sd hypothetical distribution under The problem, of course, is that you never know whether H 0 is true of false. If you did, why would you be performing a test? 17

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22 0.5% acceptance region for b 2 TYPE I ERROR AND TYPE II ERROR 5% level 1% level actual distribution under b2b2 2 +2sd 2 +sd 22 2 -sd 2 -2sd hypothetical distribution under If H 0 happens to be true, using a 1% test instead of a 5% test greatly reduces the risk of making a Type I error (you cannot make a Type II error). 18

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22 0.5% acceptance region for b 2 TYPE I ERROR AND TYPE II ERROR 5% level 1% level actual distribution under b2b2 2 +2sd 2 +sd 22 2 -sd 2 -2sd hypothetical distribution under However, if H 0 is false, using a 1% test instead of a 5% test increases the risk of making a Type II error (you cannot make a Type I error). 19

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Copyright Christopher Dougherty These slideshows may be downloaded by anyone, anywhere for personal use. Subject to respect for copyright and, where appropriate, attribution, they may be used as a resource for teaching an econometrics course. There is no need to refer to the author. The content of this slideshow comes from Section 2.6 of C. Dougherty, Introduction to Econometrics, fourth edition 2011, Oxford University Press. Additional (free) resources for both students and instructors may be downloaded from the OUP Online Resource Centre Individuals studying econometrics on their own and who feel that they might benefit from participation in a formal course should consider the London School of Economics summer school course EC212 Introduction to Econometrics or the University of London International Programmes distance learning course 20 Elements of Econometrics

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