where is the inverse Student t distribution value, that is the point in the Student t distribution for which probability is in the tails. We set to10% since we which to α α implement single-sided t-test with a 95% level of significance (i.e. 5% in one tail). is the number of scores (i.e. 12) n is the sample standard deviation of the 12 Diff Grade scores listener’s is the sample mean of the 12 Diff Grade scores listener’s If the statistic for the listener then we conclude, with a 95% confidence, that the listener i, cannot reliably differentiate between the Hidden Reference and the System under Test, and the 12 listener responses are removed from consideration. MUSHRA Test 2, Test 3 and Test 4 use the MUSHRA test methodology. For each listener in each test, post-screening was based on listener scores for Hidden Reference and Low Pass filtered anchors. The procedure is as follows: If, for any test item in a given test, either of the following criterion are not satisfied: The listener score for the hidden reference is greater than or equal to 90. That is HR >= 90. The listener scores the hidden reference, the 7.0 kHz lowpass anchor and the 3.5 kHz lowpass anchor are monotonically decreasing. That is, HR >= LP70 >= LP35. Then all listener responses in that test are removed from consideration. Statistical analysis The statistical analysis of test scores follows standard statistical procedures. The calculation of the averages over the post-screened listener scores results in the Mean Subjective Score (MSS). The first analysis step of the results considers the calculation of the mean score , for each of the presentations: where: is the score of subject for a given test condition and test item i j k. is the number of subjects N Confidence intervals were derived from the standard deviation and the size of each sample. The 95% confidence interval for a given test condition and test item is given by: j k where and the sample standard deviation is given by: With a probability of 95%, the absolute value of the difference between the experimental or sample mean score and the mean score (for a very high number of observers) is within “true” the 95% confidence interval, on condition that the distribution of the individual scores are approximately Gaussian.
where is the inverse Student t distribution value, that is the point in the Student t distribution for which α probability is in the tails. We set α to10% since we which to implement single-sided t-test with a 95% level of significance (i.e. 5% in one tail). n is the number of scores (i.e. 12) is the sample standard deviation of the listener’s 12 Diff Grade scores is the sample mean of the listener’s 12 Diff Grade scores If the statistic for the listener i, then we conclude, with a 95% confidence, that the listener cannot reliably differentiate between the Hidden Reference and the System under Test, and the 12 listener responses are removed from consideration. MUSHRA Test 2, Test 3 and Test 4 use the MUSHRA test methodology. For each listener in each test, post-screening was based on listener scores for Hidden Reference and Low Pass filtered anchors. The procedure is as follows: If, for any test item in a given test, either of the following criterion are not satisfied: The listener score for the hidden reference is greater than or equal to 90. That is HR >= 90. The listener scores the hidden reference, the 7.0 kHz lowpass anchor and the 3.5 kHz lowpass anchor are monotonically decreasing. That is, HR >= LP70 >= LP35. Then all listener responses in that test are removed from consideration. Statistical analysis The statistical analysis of test scores follows standard statistical procedures. The calculation of the averages over the post-screened listener scores results in the Mean Subjective Score (MSS). The first analysis step of the results considers the calculation of the mean score , for each of the presentations: where: is the score of subject i for a given test condition j and test item k. N is the number of subjects Confidence intervals were derived from the standard deviation and the size of each sample. The 95% confidence interval for a given test condition j and test item k is given by: where and the sample standard deviation is given by: With a probability of 95%, the absolute value of the difference between the experimental or sample mean score and the “true” mean score (for a very high number of observers) is within the 95% confidence interval, on condition that the distribution of the individual scores are approximately Gaussian.