dnewton3
Staff member
Originally Posted By: JoelB
I feel like you are trying too hard to make some black and white conclusion here. The vehicles have drastically different miles on the engines, driven by 2 different people, potentially different previous service history, different driving routes (even if they are similar). This 1 UOA hasn't proved anything beyond the shadow of a doubt.
Again, this is MACRO data driven.
All those variables (time, miles, use, environment, etc) are already taken into account in macro data. My macro data comes from 4.6L engines all over this country, in all manner of use such as daily drivers, taxis, police vehicles, grocery getters, highway cruisers, etc. In all manner of temps such as FL, AK, MN, TX, etc. Using everything from cheap dino oils to boutique high-end syns. You, and others, seem to want to discount the validity of the data, because you don't understand the nature of macro data analysis.
Different driving routes? Seriously? Did you not read my description of use? My wife and I drive from home to the same area of Indy to work. It's a 31 mile trip one-way. Once we exit the interstate, she goes east for less than 2 miles with a few stops. I go west about 3 miles with a few stops. If you believe that the variance here is enough to cause disparity in an OCI, that there is enough difference to cause a statistical variation outside of standard deviation, you are, frankly, crazy. On the days my wife works at home occasionally, I take her car to my work to even out the miles. How much "difference" do you REALLY believe exists here? This isn't a white-coat clean-room lab experiment here; it's REAL LIFE. And if it is your contention that this minor driving difference actually makes a difference in a UOA, then what would be you position to ever compare/contrast any two UOAs ever posted in the history of BITOG? In fact, given your apparent penchant for the need for EXACT SAME conditions, how could even the same car, UOA'd over and over, ever be studied? Can you assure us all that even one single vehicle which has multiple UOAs has had the EXACT same driving route every single time? (Like they never, ever deviated to the golf course, or the doctor's office, or the hospital?) Using the exact same fuel source? With the exact same traffic loading? And the exact same rain/snow/sun/temps every single day of the OCI? Etc? Come on .... you're wanting to nit-pick something that is ridiculous, and goes to show your ignorance of what macro data is all about. Different service history and accumulated miles? Not as much as you'd think.
The wear rates tell the story here; the wear rates are very similar, to the extent they can be called statistically "same" (within one standard deviation). The wear rates are telling us that DESPITE the differences in use/drivers/miles/fuel/lube and amount of McD's fries spilled on the carpet annually, they are wearing "same as".
Maybe you'd also like to question the weight differential between myself and my wife? After all, loading is a factor too, right? I'll let you ask her how much she weighs ...
Read this:
https://bobistheoilguy.com/used-oil-analysis-how-to-decide-what-is-normal/
Micro analysis looks at one specific entity, and lets data develop as inputs affect it. An example of this would be doing a series of UOAs on one engine, using a consistent brand/grade of lube, with reasonably consistent usage patterns. As much as practical, all inputs (lube, fuel, filtration, UOA sample cycle, etc) are held constant (or with minimal change), so that we can see the natural development of information. We do this to establish ranges and allow for any trends to develop. Over time, this methodology can be used to decide which product or process excels over another for any single specific application. It is very important to note that even when experiencing extremely consistent conditional and resource inputs, there is variation, even when the process is in control. We need a great deal of data from this single source to well define what is average and normal; it takes much time, money and patience to get there.
Macro analysis looks at not one entity, but all those in a desired grouping, and models not the individual effects, but rather details or predicts the behavior (results) of the mass population reaction to changing conditions (multiple inputs). Here, we can look at a large group of UOAs that represent a piece of equipment (engine, gearbox, differential, transmission, etc.) from different points of origin, and seek out what is “normal” across a broad base of applications. This approach is frequently used; it is predominant in the development of many products, from medical trials, to common electronics, to appliances, to automobiles, to consumable items like toothpaste and drinking water. The list is nearly endless as to how macro analysis can be applied. And as long as the precepts and limitations are understood, proper conclusions can be made. Macro analysis comes much quicker because multiple sources are accepted. Caution must be given, however, to make sure that illogical conclusions are not drawn, based upon false presumptions, or in confusing correlation with causation.
The point is that MACRO DATA takes into account all the typical variation of daily events in REAL LIFE. All your objections are already accounted for, sir.
These two UOAs prove beyond any doubt that they exhibited statistically normal response, despite the differences in lubes (cheap house brand dino and a brand-name syn). The conclusion drawn is valid: while we cannot state that either lube did "better" than the other, we can surely state that neither lube did "better" than the other.
That you, and some others, don't understand this distinction does not make it any less true.
I feel like you are trying too hard to make some black and white conclusion here. The vehicles have drastically different miles on the engines, driven by 2 different people, potentially different previous service history, different driving routes (even if they are similar). This 1 UOA hasn't proved anything beyond the shadow of a doubt.
Again, this is MACRO data driven.
All those variables (time, miles, use, environment, etc) are already taken into account in macro data. My macro data comes from 4.6L engines all over this country, in all manner of use such as daily drivers, taxis, police vehicles, grocery getters, highway cruisers, etc. In all manner of temps such as FL, AK, MN, TX, etc. Using everything from cheap dino oils to boutique high-end syns. You, and others, seem to want to discount the validity of the data, because you don't understand the nature of macro data analysis.
Different driving routes? Seriously? Did you not read my description of use? My wife and I drive from home to the same area of Indy to work. It's a 31 mile trip one-way. Once we exit the interstate, she goes east for less than 2 miles with a few stops. I go west about 3 miles with a few stops. If you believe that the variance here is enough to cause disparity in an OCI, that there is enough difference to cause a statistical variation outside of standard deviation, you are, frankly, crazy. On the days my wife works at home occasionally, I take her car to my work to even out the miles. How much "difference" do you REALLY believe exists here? This isn't a white-coat clean-room lab experiment here; it's REAL LIFE. And if it is your contention that this minor driving difference actually makes a difference in a UOA, then what would be you position to ever compare/contrast any two UOAs ever posted in the history of BITOG? In fact, given your apparent penchant for the need for EXACT SAME conditions, how could even the same car, UOA'd over and over, ever be studied? Can you assure us all that even one single vehicle which has multiple UOAs has had the EXACT same driving route every single time? (Like they never, ever deviated to the golf course, or the doctor's office, or the hospital?) Using the exact same fuel source? With the exact same traffic loading? And the exact same rain/snow/sun/temps every single day of the OCI? Etc? Come on .... you're wanting to nit-pick something that is ridiculous, and goes to show your ignorance of what macro data is all about. Different service history and accumulated miles? Not as much as you'd think.
The wear rates tell the story here; the wear rates are very similar, to the extent they can be called statistically "same" (within one standard deviation). The wear rates are telling us that DESPITE the differences in use/drivers/miles/fuel/lube and amount of McD's fries spilled on the carpet annually, they are wearing "same as".
Maybe you'd also like to question the weight differential between myself and my wife? After all, loading is a factor too, right? I'll let you ask her how much she weighs ...
Read this:
https://bobistheoilguy.com/used-oil-analysis-how-to-decide-what-is-normal/
Micro analysis looks at one specific entity, and lets data develop as inputs affect it. An example of this would be doing a series of UOAs on one engine, using a consistent brand/grade of lube, with reasonably consistent usage patterns. As much as practical, all inputs (lube, fuel, filtration, UOA sample cycle, etc) are held constant (or with minimal change), so that we can see the natural development of information. We do this to establish ranges and allow for any trends to develop. Over time, this methodology can be used to decide which product or process excels over another for any single specific application. It is very important to note that even when experiencing extremely consistent conditional and resource inputs, there is variation, even when the process is in control. We need a great deal of data from this single source to well define what is average and normal; it takes much time, money and patience to get there.
Macro analysis looks at not one entity, but all those in a desired grouping, and models not the individual effects, but rather details or predicts the behavior (results) of the mass population reaction to changing conditions (multiple inputs). Here, we can look at a large group of UOAs that represent a piece of equipment (engine, gearbox, differential, transmission, etc.) from different points of origin, and seek out what is “normal” across a broad base of applications. This approach is frequently used; it is predominant in the development of many products, from medical trials, to common electronics, to appliances, to automobiles, to consumable items like toothpaste and drinking water. The list is nearly endless as to how macro analysis can be applied. And as long as the precepts and limitations are understood, proper conclusions can be made. Macro analysis comes much quicker because multiple sources are accepted. Caution must be given, however, to make sure that illogical conclusions are not drawn, based upon false presumptions, or in confusing correlation with causation.
The point is that MACRO DATA takes into account all the typical variation of daily events in REAL LIFE. All your objections are already accounted for, sir.
These two UOAs prove beyond any doubt that they exhibited statistically normal response, despite the differences in lubes (cheap house brand dino and a brand-name syn). The conclusion drawn is valid: while we cannot state that either lube did "better" than the other, we can surely state that neither lube did "better" than the other.
That you, and some others, don't understand this distinction does not make it any less true.
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