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This paper analyzes how an experimenter can balance errors in subjective video quality tests between the statistical power of finding an effect if it is there and not claiming that an effect is there if the effect it is not there i.e. balancing Type I and Type II errors. The risk of committing Type I errors increases with the number of comparisons that are performed in statistical tests. We will show that when controlling for this and at the same time keeping the power of the experiment at a reasonably high level, it will require more test subjects than are normally used and recommended by international standardization bodies like the ITU. Examples will also be given for the influence of Type I error on the statistical significance of comparing objective metrics by correlation.