# What Test Do I Use?

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• #54015

JAshouri
Guest

Hi There,
I am working a project on returned samples for our soda cans. We send out free samples to large corporations. Some are sent AUTO SHIPPED – and some are sent by our Sales group. I have data that shows the defects for AUTO SHIPPED and by our sales group. A defect is any soda can sample that is returned (Due to various reasons). I wanted to see if there is a statistical difference between defects shipped by AUTO vs SALES. I thought the best test is the CHI SQUARE. IS that correct? Please advise…thanks experts..

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#192942

Chris Seider
Participant

Sounds good to me. Just be sure to use # of cans sent defectively and # of cans sent correctly for both categories. As long as you have a minimum count of 5 for each of the four cells in the chart, you can use it.

A more powerful analysis would be to count individually the # of defective for EACH defect type and you can still use chi-squared as long as each cell has at least 5. Just be sure to NOT have a combined row of cells for total defects.

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#192943

MBBinWI
Participant

@jashouri – Do you have just the 2 categories (auto and sales)? How many samples in each and how many defects in each? And you only count defectives, not defects (any return for any reason is a defective)?

You’re probably good with a Chi-sq if the number of levels is low (2 categories with whole number sample size and whole number defects would likely fit the bill).

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#192945

JAshouri
Guest

Thank You to both of you. Below are my results and I will describe what I did

All I did was take the Defective count for Sales and the clean ones vs the Defective count for AUTO and the clean one and ran the Chi Square. I did not have more than 5 samples etc…I just took TOTALS. Is that wrong? The P Value is below .05 so this confirms there is a statistical difference. I do have the totals split up by month if that means anything or equates to more than 5 samples…Did I do this correctly and is my assumption correct? See below the chi square results

Clean_1 Defective_1 Total
Sales 2043875 19581 2063456
2041800.24 21655.76
2.108 198.776

Auto 1312743 16020 1328763
1314817.76 13945.24
3.274 308.682

Total 3356618 35601 3392219

Chi-Sq = 512.841, DF = 1, P-Value = 0.000

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#192951

MBBinWI
Participant

@jashouri – what you did was exactly right. If you want to peel the onion further, you could split these up by relevant “by” variables (you indicate that month might be appropriate).

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#192953

jamil
Participant

Thank You. Last question. So the p-Value shows there is a statistical difference between if Sales send out a sample vs the sample being AUTO shipped. So the conclusion – is that this is a root cause correct? Are there other tests to do statistically? Or do I now peel the onion further and dig deeper into EACH PROCESS? I guess I am asking – now that it is statistically different – what are the next steps? I GREATLY APPRECIATE THE HELP

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#192954

MBBinWI
Participant

@jashouri – This only shows that there is a statistical difference (at 95% CI) that there is a difference in expected levels vs. actual. You will need to figure out why using different tools (5 whys would be good here). If you have other by variables (such as month) then you may want to evaluate those similarly and see if there is anything that comes out (maybe there was a shift during a certain month – if so, then you can investigate what happened in that time frame). Right now, look at what’s different between auto and sales.

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#192955

MBBinWI
Participant

@jashouri – you might also want to Pareto the reason for the return. Your sales people have fewer than expected defects, so they are probably culling out non-productive customers, so find out why.

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#192961

Stevo
Member

I dont really like to read, this might have been said. And you already have two big fish on the line….

But  One of my pet peeves is jumping to a stat tool early. Are you working with a percent or did both groups have the same amount of samples. Regardless  If you want to see if there is a difference and you know that one group is 500 and the other is 3 or 90% vs. 7%. Stop. Or do some graphical work. Stop. Then, if you still have questions jump in a more advanced tool.

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#192978

Mike Carnell
Participant

@jashouri You still seem to be at a very high level. Do you have data on where, when or how they were sent?

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#193013

Ravindra Joshi
Guest

@Jashouri – Do you have the accessibility of how , when & under what conditions thses cans are produced. ARE the cans sent by AUTO SHIPPED & Sales manufactured under same conditions, if yes then probably you can do the GRR as well in my opinion to find out is the defect because of variations during producing cans, u might pick up only defect cases & do the study on the same. Meanwhile as suggested by others if u have reasons please go ahead with Pareto, I will also suggest you to do DFMEA ti see where all the design can fail.

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#193020

Mike Fisher
Guest

2 proportions test could be thrown into the mix as well…

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