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<blockquote data-quote="Andy Ful" data-source="post: 1022808" data-attributes="member: 32260"><p>It is worth knowing that the probabilistic model used in the sample size calculator is different from the probabilistic scenario of AV tests. The first is time-independent. The second is generally time-dependent.</p><p>Let's assume that the test is similar to the AV-Comparatives R-W tests (600 samples per 4 months). When we draw 600 samples from the population <span style="color: rgb(184, 49, 47)"><strong>Samples</strong></span> in the wild then:</p><ol> <li data-xf-list-type="ol">In the first case, it is possible that among these 600 samples, there will not be any <strong><span style="color: rgb(184, 49, 47)">Samples</span></strong> from the first day of the test. The same is true for any particular day (or group of days) of testing.</li> <li data-xf-list-type="ol">In the second case among these 600 samples, we can always find a few (approximately 5) <strong><span style="color: rgb(184, 49, 47)">Samples</span></strong> from each day.</li> </ol><p>In the calculator, we can have only one parameter "Population Proportion". In the realistic test, we can have 120 such parameters. When we use the sample size calculator for the real AV testing data, a kind of average "Population Proportion" must be used. In my examples, I used the average from long-term data (two-year testing data). The more appropriate average would be over the period of testing (but hardly possible).</p><p></p><p>Final conclusion.</p><p>Thank [USER=97327]@Max90[/USER], I examined the possibility of using a sample size calculator. This simple statistical model works surprisingly well, although it is a rough approximation of the real scenario and it is necessary to use also long-term data (like in the OP). It seems that this combination roughly confirms the necessity of using some AV clustering in the R-W tests. The number of tested samples is insufficient to reliably differentiate between several AVs contained in the same cluster. Such clustering is done for several years by AV-Comparatives, AV-Test, and SE Labs.</p><p></p><p>The calculations with the "sample size calculator" partially confirm that a two-year cumulative statistic is usually required to find statistically significant differences between AVs. In some cases, a three-year (or more) testing period is needed. But, using a three-year or more, can be accepted only in the case when the AVs protection rate does not change significantly. So I probably stay with a two-year long-term period.</p><p></p><p>Post edited for more clarity (in red color).</p></blockquote><p></p>
[QUOTE="Andy Ful, post: 1022808, member: 32260"] It is worth knowing that the probabilistic model used in the sample size calculator is different from the probabilistic scenario of AV tests. The first is time-independent. The second is generally time-dependent. Let's assume that the test is similar to the AV-Comparatives R-W tests (600 samples per 4 months). When we draw 600 samples from the population [COLOR=rgb(184, 49, 47)][B]Samples[/B][/COLOR] in the wild then: [LIST=1] [*]In the first case, it is possible that among these 600 samples, there will not be any [B][COLOR=rgb(184, 49, 47)]Samples[/COLOR][/B] from the first day of the test. The same is true for any particular day (or group of days) of testing. [*]In the second case among these 600 samples, we can always find a few (approximately 5) [B][COLOR=rgb(184, 49, 47)]Samples[/COLOR][/B] from each day. [/LIST] In the calculator, we can have only one parameter "Population Proportion". In the realistic test, we can have 120 such parameters. When we use the sample size calculator for the real AV testing data, a kind of average "Population Proportion" must be used. In my examples, I used the average from long-term data (two-year testing data). The more appropriate average would be over the period of testing (but hardly possible). Final conclusion. Thank [USER=97327]@Max90[/USER], I examined the possibility of using a sample size calculator. This simple statistical model works surprisingly well, although it is a rough approximation of the real scenario and it is necessary to use also long-term data (like in the OP). It seems that this combination roughly confirms the necessity of using some AV clustering in the R-W tests. The number of tested samples is insufficient to reliably differentiate between several AVs contained in the same cluster. Such clustering is done for several years by AV-Comparatives, AV-Test, and SE Labs. The calculations with the "sample size calculator" partially confirm that a two-year cumulative statistic is usually required to find statistically significant differences between AVs. In some cases, a three-year (or more) testing period is needed. But, using a three-year or more, can be accepted only in the case when the AVs protection rate does not change significantly. So I probably stay with a two-year long-term period. Post edited for more clarity (in red color). [/QUOTE]
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