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Hard Data v Gut Feel Expand / Collapse
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Posted Tuesday, July 10, 2012 10:12 AM


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If you can show that there is data to support a decision, it is more likely to be well received than one based on the experience, instinct, or hunches of any person.


I agree with that statement. Even though experience should have significant import, it's the numbers that matter - or so it seems.




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Post #1327731
Posted Tuesday, July 10, 2012 10:21 AM
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SQLRNNR (7/10/2012)
If you can show that there is data to support a decision, it is more likely to be well received than one based on the experience, instinct, or hunches of any person.


I agree with that statement. Even though experience should have significant import, it's the numbers that matter - or so it seems.


But experience can tell you when to trust the numbers, and how much weight to give them.


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Post #1327741
Posted Tuesday, July 10, 2012 10:27 AM


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jay-h (7/10/2012)
SQLRNNR (7/10/2012)
If you can show that there is data to support a decision, it is more likely to be well received than one based on the experience, instinct, or hunches of any person.


I agree with that statement. Even though experience should have significant import, it's the numbers that matter - or so it seems.


But experience can tell you when to trust the numbers, and how much weight to give them.


As long as you look at the numbers. Too many people ignore them







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Post #1327750
Posted Tuesday, July 10, 2012 11:27 AM
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The movie "Moneyball"* is about this same topic, except in the realm of professional baseball. Basically, Moneyball is about the application of Business Intelligence/Analytics to baseball.

One interesting thing the movie showed is that it's not just using numbers which matters, but which numbers you are using that can be even more important. It's not like the "old school" managers and scouts weren't using numbers to evaluate players, but they were using metrics like RBIs and Batting Averages. The Oakland A's started using metrics like On-base Percentages and found they could better predict a player's future success with these kinds of numbers. And since these metrics were undervalued in general by pro baseball, the players who had good On-base Percentages, etc. were also undervalued.

Their low budget for player salaries was what really drove the A's to have to find a way to compete with teams like the New York Yankees who could spend a bundle on players. Oakland had some amazing success using these new metrics, and the other teams were left scratching their heads about how the A's could do what they were doing with the kinds of players they had signed, at the salary levels they were able to pay.

* Oh yeah... I think there was a book by the same name too.
Post #1327782
Posted Tuesday, July 10, 2012 11:47 AM


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Steve Jones - SSC Editor (7/10/2012)
jay-h (7/10/2012)
SQLRNNR (7/10/2012)
If you can show that there is data to support a decision, it is more likely to be well received than one based on the experience, instinct, or hunches of any person.


I agree with that statement. Even though experience should have significant import, it's the numbers that matter - or so it seems.


But experience can tell you when to trust the numbers, and how much weight to give them.


As long as you look at the numbers. Too many people ignore them


At least partially because they are too often better off ignored.

Weather forecasts for example. Studies have been done that show that flipping a coin is more accurate.

Decades of weather forecasts were analyzed for the simple measure, "If the forecast said x-percent chance of rain tomorrow, how often did it actually rain the next day?" For example, if it said 10% chance of rain, and it rained 15% of the time on the following days, then it was incorrect by 50%. If it said 50% rain, and it rained 45% of the days, then it was off by 10%. And so on. This was then measured against flipping a coin, "heads it'll rain tomorrow, tails it won't". The coin-tosses had better overall accuracy.

Huge, well-funded industries depend on weather forecasts. Aggriculture, shipping, energy, to name a few. The R&D dollars are there. Vast amounts of data are gathered by local weather stations, satelites, and so on. And the accuracy is still lower than a random guess when taken over any meaninful period of time.

Hurricane-season "predictions" are modified repeatedly throughout the year, and still end up wrong by the end of the year more often than not.

Moving out of the weather, take a look at the US CBO. The non-partisan Congressional Budget Office. Math and finance wizards with more PhDs than you can shake a stick at. They have all the access to vast amounts of information. And, so far as I can tell, they've never been right yet about anything. They crunch huge amounts of data, they use the most widely accepted and practiced algorithms, and they're consistently off by huge margins, even when they aren't being made to load the numbers a certain way for some political agenda.

On the other hand, insurance actuary tables are amazingly accurate, but only on large systems of data. X-people will likely die of Y cause in Z time period, given large populations with known smoking, eating, etc., habits. (Smoking and income-level being the two biggest factors, if I remember correctly. Gender might be right up there, I'm not sure.) Can't predict the age-of-death of any given individual with any accuracy, but that's not what they're for.

Horse racing is another one where real odds can actually be predicted. And, of course, poker and blackjack give the odds to the house or they wouldn't be played in Vegas.

So, you have to know whether you're dealing with something simple like risk factors associated with smoking, or winning poker hands, or with anything that involves complex systems, like weather, or systems that are very poorly studied and often intentionally muddied, like money+politics.

But in many of these systems, ignoring the "data" is quite likely the best option. After all, if a coin toss is more accurate, then the data is beyond being simply flawed, and is actually likely to increase your chances of error.

Keep in mind, though, that all of what I just wrote is written by a serious data-junkie (me), and may be skewed by my prejudices on the subject. (On the other hand, I've accurately predicted every US presidential election since I started noticing them in the '80s. So maybe I'm slightly more accurate than a coin-toss. Who knows?)


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Post #1327788
Posted Tuesday, July 10, 2012 5:02 PM


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GSquared (7/10/2012)
Steve Jones - SSC Editor (7/10/2012)
jay-h (7/10/2012)
SQLRNNR (7/10/2012)
If you can show that there is data to support a decision, it is more likely to be well received than one based on the experience, instinct, or hunches of any person.


I agree with that statement. Even though experience should have significant import, it's the numbers that matter - or so it seems.


But experience can tell you when to trust the numbers, and how much weight to give them.


As long as you look at the numbers. Too many people ignore them


At least partially because they are too often better off ignored.

Weather forecasts for example. . . .

Weather is apparently manipulated, so IMO it is not surprising that predictors based on pre-manipulated weather do not work.
Post #1327991
Posted Thursday, July 12, 2012 3:59 AM


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One of my favourite advocates of an evidence based approach is Ben Goldacre and his Bad Science blog. He is a doctor, so fairly focused on clinical trials and interpretation thereof, but often covers (UK) government or newspaper foolishness. It's quite a geek-out with extensive coverage of recently for instance Benford's Law and it's application to detecting electoral fraud.
Post #1328777
Posted Thursday, July 12, 2012 6:39 AM


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Revenant (7/10/2012)
GSquared (7/10/2012)
Steve Jones - SSC Editor (7/10/2012)
jay-h (7/10/2012)
SQLRNNR (7/10/2012)
If you can show that there is data to support a decision, it is more likely to be well received than one based on the experience, instinct, or hunches of any person.


I agree with that statement. Even though experience should have significant import, it's the numbers that matter - or so it seems.


But experience can tell you when to trust the numbers, and how much weight to give them.


As long as you look at the numbers. Too many people ignore them


At least partially because they are too often better off ignored.

Weather forecasts for example. . . .

Weather is apparently manipulated, so IMO it is not surprising that predictors based on pre-manipulated weather do not work.


How'd you know weather is manipulted? Fnord You must forget that Fnord


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Post #1328841
Posted Thursday, July 12, 2012 7:55 AM
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One of the things I found out early on was was that decision makers often ignore data in favor of their pre-existing desires and prejudices.

I created a fairly detailed analysis once that showed that replacing our main VAX server with a new server would save about $200,000 over the first 3 years, and that the net cash flow would be positive from day one. Basically, it would result is a savings in cash from day one, and would generate a large labor performance improvement because the old system was so overloaded that people were spending far more time doing their work than they should be.

I thought this would be a no brainer because the reduction in monthly maintenance cost on the current system would be greater than the lease cost for the new system.

My proposal was immediately bogged down with emotional counter arguments: “Shouldn’t we be moving to UNIX?” “Why don’t we just let end-users do everyting on PCs with spreadsheets?” “This is just IT wasting money that we should be spending in my department”

We eventually went forward with the proposal, but not until there was a major turnover in decision makers.

Since then, I always try to make sure that I understand the emotional context of the decision makers and make sure my proposals fit their prejudices along with having hard financial analysis support. Of course the danger of playing to their emotions is that I may get them to make a financially bad decision, so I try to make sure the data really supports it, and that I am not pushing my own prejudices.




Post #1328898
Posted Friday, July 13, 2012 6:48 AM
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Michael Valentine Jones (7/12/2012)
One of the things I found out early on was was that decision makers often ignore data in favor of their pre-existing desires and prejudices...



When people want a conclusion they can often find 'hard data' to support it. One by careful 'categorization' of data you can provide evidence that a project will pay off. If there's not enough immediate payoff, keep looking at secondary and tertiary 'benefits' until the numbers come out right.

I remember reading an article some time back about a city who wanted to justify promotion of an 'arts district'. They just included more and more of the area's commerce into the equation (as a benefit of the district) until they got the numbers they wanted. The fact that those benefits might not come from the project, or would come regardless of whether the project was implemented were conveniently ignored.


...

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