Actually, you

*should* consider

*expected distributions* prior to analyzing your data. First you should prepare your

*total variance equation* to determine what variances you expect, and what distributions they are likely to generate. The process will be the result of the sum of those distributions. Effort to reduce the effects of distributions from gage, measurement and other minor process variances to prevent masking of the the main variances is

*key.*
Then do your time-ordered sequence chart. Look for signs of a function (variation as a function of time, such as a rate; e.g. tool wear rate, heating rate, etc.), such as runs in one direction or another throughout the chart. If you are not getting your expected distribution, you may need to go back to the equation and either find or reduce the effect of the other variances before you can ever claim control or determine randomness

*and* independence. After all, you can often be in control and not random and independent if you can identify your process output as a function. And, if that is the case, you can not use Shewhart charts to monitor that kind of process. If will yield signals exactly opposite of the true process condition.

Of course, curve fitting is a far more straight forward analysis of the resulting distribution than probability plot.

Rummaging through the data as is without this thought process will not be the best use of time.