Adam is wrong here.

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Assuming the sample normal distribution is necessarily m, the confidence interval is CI = m ± t*SE, where t is a t-type quantile with n-1 degrees of freedom. The two-sided (1-a)-CI is derived from this (1-a/2)-quantile. More generally, if m can be k coefficients in this linear model, a t-distribution is used, given n-k degrees of freedom.

System can be solved for OS:

CI_{upper} = m + t*SE —> SE = (CI_{upper}-m)/t

The T-quantile can be found for the confidence level when implementing the total sample diameter (n) and the number of coefficients behind the model (k). For example, for the t-quantile to have 95% confidence, n=10 and k=2 is 2.3. For large n-quantiles, the capacity is 2.0 (well, 1.959964… to be precise; but 2 with .0 is pretty impressive).

If the underlying sales model is unlikely to be the normIn other words, the construction in terms of CI is different, and therefore it is difficult to obtain SE (and depends on the marketing model). However, not knowing the meaning of your current SE is very important when it comes to abnormal distribution patterns.