Synergy

In business, most of the time, it doesn’t happen. People tend to work on their own area and own sides leaving two or more extremely strong wheels to spin on their own.

As an analyst who was initially trained in a “conservative” setting, I find that it is sometimes hard to create synergy with those who do not think in tight theoretical ways. When I first started my career, I was pretty idealistic and very conservative with data. All things had to be “balanced.” This made it extremely difficult to create synergy with those who do not have to have things balanced. As I moved on with my career, I quickly realized that more flexibility was needed on my side. This was accelerated when I began working for a photomask company as the corporate statistician. You see, photomask manufacturing is pretty much N-of-1 manufacturing, and as you know statistics in manufacturing is all about replication. So, I quickly found out the key was not to focus on what was different and how to “fit” a statistical model to non-replicates, but to find out really what was the same, what WAS replicated, and control the heck out of that. It worked well, and led to my first two publications. However, I never really was part of a synergy, since my main focus was to pound out reports and focus on how to analyze the same data differently.

I then moved to my current position, and marketing was a new area of focus for me. It was much more fluid than the “lab” of a research facility. What I did realize, however, is that there was some synergy happening within the company. People were working together, not in silos. Lately, we are really starting to pick-up momentum, and it is very exciting. Things are “coming together” in a way few analysts actually get to see. Most of us typically sit back and pound reports and think of new ways to get and analyze data. What is really exciting though is when the metrics begin to line up with the corporate identity. That is a great feeling for an analyst.

So, what’s my point? To create synergy, everyone needs to change and it takes time. It took me years and different situations, to change from a “theoretical” statistician and come closer to the middle. Yet, it can’t just be one person. It will not work if one person moves all the way to the other person. Others also have to come towards the middle. When it does happen though, it can be a very exciting time for everyone.

More Polls

OK, this primary on the democratic side is going to be a wild one (at least in the news). We have another “swing” with a poll. The CNN/Opinion Research Poll that was reported today has indicated that Obama has now “erased” a gap between himself and Clinton with one day to go. Compare this to a few months ago when Clinton had a “significant” lead over Obama. Meaning? Not much.

Here’s why. Although I appreciate that they state the current poll has a 4.5 point error rate and do not say he is now in the lead, it means little to nothing as to how things will shake out tomorrow. This is a national survey. So, they are again sampling from a population who may not even live in a state that will vote tomorrow, and even if he or she does, who knows if they will even vote in a primary. So, it means nothing to the amount of delegates that Obama or Clinton could pick up. But it sure makes for great headlines, which is the scary part. By dissecting each poll and showing these “wild swings” the media is creating news, not reporting it. For the casual observer, if they see this, they may decide to hitch themselves on the wagon of the winner and it may have a small effect on the outcome tomorrow.

Maybe more interesting is the vote in California, who is voting tomorrow. There was a large fluctuation between two weeks ago when Clinton had a double-digit lead to a poll on Sunday that shows an insignificant lead for Clinton (within the 4.5 points). How interesting is this? Not as interesting as they want it to seem. Could it be Oprah? Could it be Maria Shriver? Or could it just be bad sampling. I am still on point to say that a poll should not fluctuate this much within a two week period if the sampling is right (whether the premise, delivery or results of the poll is right or not.) Bottom line, anytime you are sampling from the same population there should not be such a fluctuation, even if you are asking the wrong thing. In my work, the first thing I look to when I see something like we see here is, “Did I get my sampling right?” “Am I asking the same questions from the same population?” In the case of these polls, I say probably not.

So, sorry, maybe Oprah isn’t responsible for such a wild swing after all. Who could be? Hmmm, didn’t Edwards just drop out in the last two weeks? A point that is lost on them I suppose…

Primary Polls

So, why have political polls been so wrong? I get asked this from time to time. Well, it is a complicated answer. First, we must address which polls have the problems. The first type of polls, (the ones that seem so wrong) are the pre-voting polls that are taken weeks or days prior to polling. The second type, the exit polls, is taken directly after the voter has voted. Obviously, this one is much more accurate (although the last two elections, even these are failing much more frequently.)

Let’s focus on the pre-voting polls. These polls are taken months, weeks, and days before the vote. To keep things simple, people from a particular demographic are sampled and polled about who they would vote for in the upcoming primary. In the recent primaries, they have been WAY off. Why? Well, it can be quite complicated, but I think there are several things at play. First, the models are broken. Many people are still living in a world where they think that the old social model is still in existence. This is not true. No longer can we typecast people according to strict demographics. Where you could once count on a particular demographic to react or vote one way, you can no longer do so. Why? People have so much more information at their finger types due to technology. In years past, people would get their information from regional and perhaps one national news source and they could be swayed easier since they only got a couple of views. Now, people are inundated with news 24 hours a day 7 days a week. They also no longer have to count on social networking with people in their vicinity, but rather can converse with people from all across the world who actually hold many of their views, creating micro-groups of people with the same thoughts. One-person on an island no longer exists. In other words, the old models are no longer accurate. This leads to the second issue which is sampling. If the models are broken, surely the sampling is as well. When you rely on asking a few people to predict the whole, you must have the correct samples in place. Because of what was stated above, undoubtedly the samples are wrong. How can you tell? Look and see how fast the same polls are changing from week to week or in some cases day to day. One polling center can have wild swings. This is no fluke. If your sample is not accurate, this can happen anytime you are attempting to predict. Furthermore, when polling a primary, you may be asking people who have no plan on voting in the primary. Another reason for the wild swings? Because of the information explosion, people tend to change their mind much quicker than before. We are a society of instant news and change, which makes it that much harder to predict.

So, what to look for with Super Tuesday coming up? Well, certainly do not look too far into the polls to tell you what is going to happen. Only way to be for sure on who will come out ahead is by watching the actual results come in.

Why Statisticians Shouldn't Watch Sports

Check out this link. I got a kick out of this. First of, I really liked his reasoning. For the most part, he controlled for all the variables he needed to control for and made things simple yet elegant (I assume when he controlled for defensive points, he also controlled for only yards allowed by Defense.). Secondly, I understand this man's pain. Can't even watch a game without trying to analyze some mundane fact that only other people like him would like, which in turn causes my wife soem pain as well, having to hear it.

So, for all of you people out there that want to go check out if their team is a "bend but don't break" defense, one bit of warning. He focused on the Pac-10. So, what would be interesting to know if his ratio would stay the same in different conferences. I would assume possibly not. If not, then to have an accurate ratio, you may need to focus on each conference and then within division I football.

Demming

Earlier today I sat through a meeting in which the presenter mentioned Deming. Wow, this was the first time I have heard Deming in about 2 years, or the time period that I have spent in Marketing. Of course my interest was peaked. The presenter went on to explain how important it was to experiment every day. I totally agree with this. However, I was a little disappointed that he never spoke about how to ensure you adequately measure those results. I am giving him a pass though, as I assume it was the audience he was speaking to. Regardless, I think this is an extremely important point. Experiment all you want in this world. Tweak things and be curious...but always make sure you can adequately measure your results of the test. If not, then you have no idea what "experiment" really worked. To do this, you need to make sure your data is accurate and accessible; and that you have control of the variables. If not, you can test all you want, but you will have little understanding as to whether your manipulation effected your metrics, or something else effected them.