P chart sample size

Why aren’t you just doing a 2 proportion test if you want to look for difference?  Your sample sizes are a bit small to, as you say, invoke much meaning and confidence. 

Hi
No, you cannot be sure that you have draw the right conclusion, as your betarisk is very high, the risk of saying there is no difference although there really is one.
I propose that you, instead of waitning until you have enought data, try to find the rootcause for the defects and make a pareto and work on trying to reduce the major reasons for not closing a item.
Waitning to get ‘enough’ data will simply take to long for you, and what would the effect be if you really find a difference compared to if you dont. I mean what is the practical effect of your conclution from the hypotheis ?
You will most likely get a faster improvment by just looking on reasons for defects at a pareto, sometimes the simple solutions is the fastest,
 
 

newbie,
Sales environments like what you describe are tough places for data, but here are a couple of thoughts.  As with most things, it depends on your situation – resources, time, data availability, etc.
Consider the level of data representation required for impact.  I’ve heard the “PGA” method taught before – “Practical, Graphical, Analytical”.  Sometimes (and I try to make it rare!), practical significance must override the more detailed graphical or analytical work.  I don’t need to statistically prove that Person A outsold Person B if Person A had more sales at the end of the time period (day, week, month, etc.).  If Person A sold 59 and Person B sold 64, it’s easy for anyone to see. 
That said, there a re a LOT of things to consider before making that kind of judgment:  monoline vs. multiline sales enviornment, profitability per sale type, compensation / incentive differences, NIBT to the organization, etc.
As with a lot of things, the more basline data (sales performance information) you can gather, the better off you will be.  You’ll see most sales people center themselves into a performance range (boxplots can be a nice graphic to depict spread performance between people). 
However, do not neglect the wealth of sales process inputs: Lead quality, lead volume, tenure, incentive plans, training, education, employee satisfaction, etc.  Develop a predictive equation if you can (though don’t assume linearity!).  These are your keys to continuous process improvement efforts, and moving away from the unsustainable efforts like benchmarking best performers. 
Lastly, don’t neglect any sales fulfillment or post-sale efforts that may have an impact on your output metric of “# sales”.  If your sale is a simple transaction (thank you, here is your widget), not many worries.  If your sale metric includes an application that then gets processed, with an order fulfillment, with shipping, etc., then you need to consider these things as they relate to your overall sales metric.
Good luck, and I hope this is provides some help.
Regards,
Ritz
 

Maybe I did a little confusion with my english, so I think is better to summarize again. I think that P chart you are building can be right, but general rules, says that you could recalculate control limits for each sample size, if it changes more than 25%. This is a way to control operators process, with all Pchart limitations. About how to compare the two operators results, if I well understand data provided are a cumulative, not a single sample. If so, what I said is that you could perform the proportion test on each sample and monitor how it change. No, for Fisher’s exact test I mean an analysis of frequency data, not Anova  (see link http://faculty.vassar.edu/lowry/VassarStats.html ).
Apologies again for my poor english and let me know.
Rgs, Peppe

Hi Ritz, thanks for concern, but could you explane why it is not applicale to original question on how compare two operators performances based on number of failure and/or to expected performances ? Further, what do you suggest is the right test (we have already checked proportion test) ? My thought was to check the frequencies of failures/successes.
Rgs, Peppe

Newbie, do you have a target ? Without target which criterisa you refer on ?  Which decision you can take, even if you “detect a process shift” ? Could you provide your raw data ?
Rgs, Peppe

Looking to your data, I think that standard P chart isn’t the best way to monitor what you want (from it comes UCL and LCL excessive). I think you can calculate standard error of ‘failures’ and then use it for UCL and LCL for each operators. You’ll see different limits for different operators and out of limits will be highlighted. The test to use is F test for variance, that highligh differencies (with alpha=0,05,  p=0,02). Operator J is working better. If you provide an e-mail, I can send you a spreadsheet with some calculations.
Rgs, Peppe

Hi Pep,
Can u send me a spreadsheet with calculations? thanks lots
[email protected]
 
Michael