Political Polls—The Odds

An Ekos poll gives the Conservatives 31% of the vote and the Liberals 27.7%, +/-2.4%, 19 times out of 20. It's an interesting estimate, but not really useful for making decisions; a Liberal win is within the range of error.

Looking more closely, what these numbers say (but Ekos doesn't) is that the Conservatives are favored to win a plurality with at least 9:1 odds. Probabilities can be more useful than estimates, and they can have a dramatic impact on our understanding of the situation.

The 9:1 odds calculation assumes a Liberal/Conservative zero-sum game. Every vote gained by one party is a loss to the other. If this is relaxed and votes can be taken from or lost to the other parties, the odds favouring a Conservative win are about 40:1 (not a typo--that's forty).

These numbers come from a Monte Carlo simulation of 10,000 elections assuming the Ekos numbers represent Gaussian distributions. In the first case, the Conservatives win when they get more than 29.35% of the votes--a majority of the votes in play. In the second case, the Conservatives win when they get more votes than the Liberals.

Ekos hasn't told us enough to gauge the mobility of votes from the other parties, so we can't resolve the difference. On the other hand, they have the data so we're left wondering why they don't report the proper odds.

There's an Excel spreadsheet for this calculation and that you can use to see the odds for other polls. It includes a 1000-election simulation. With only two uncertain variables, it's about as simple an example of Monte Carlo simulation as you can get. See http://smpro.ca/crunch/PollOdds.xls

What Dooms IT Projects? —Again

Ziff's Baseline Magazine treats us to yet another slide show about IT project failure in this deck by Dennis McCafferty.

It trots out the usual suspects: Lack of user involvement, unrealistic timelines, poorly-developed requirements, etc. This time it's based on an uncited Standish Group report that seeks to explain a 35% failure rate.

As always, when you put it all together, it adds up to poor planning: when you look at what actually happened and ask what was in the plan, you find that the model bears little resemblance to the reality.

To have a chance of success, a project has to have a realistic, comprehensive plan whose model is close to what experience tells us is the reality. It needs a crystal clear objective, measurable success criteria,  intelligently estimated timelines, resource allocations that account for risk, and plans for dealing with unknowns.

I love it when a plan comes together, but not when it's by accident.

DIST Direct: Build DISTs without array ranges

To create a DIST in Excel, you need a formula that returns an array of the desired size and content. In general, this means that somewhere in the spreadsheet there's a column of a thousand or ten thousand samples to be converted.

It struck me that this shouldn't be necessary if we're using a parametric distribution (e.g. uniform, gaussian,..).

The Flaw of CPM

CPM (Critical Path Method) calculates the longest path of planned activities to the end of the project, and the earliest and latest that each activity can start and finish without making the project longer. This is so we can focus attention on the activities that can make the project late if they take too long.

The flaw in the CPM is that the conventional critical path is only valid in the unlikely case that the project's activities complete in the estimated times. In the real world, the critical path keeps changing; as the project progresses, a non-critical activity that comes in late can move the critical path to include its successors. Similarly, an activity on the critical path that comes in early can relieve its successors and cause the path to move elsewhere. Most scenarios will provide a mix of these.

An Uncertain Project Plan

If someone asks what you think the odds are of meeting your project deadline, is your answer something like "pretty good"? Do you know what your odds of success are? Do you know how much moving the deadline would change the odds?

"How long will it take?" doesn't have one right answer. It has a bunch of answers, each with its own probability of being right. We'd like to see them all before deciding on a target date.

XLSim User Group

We have an XLSim User Group on Linkedin. The URL is http://www.linkedin.com/e/vgh/2920508/eml-grp-sub/.

If you're a user or just interested, please join us.

Statistical Insignificance

Our thanks to Tom Siegfried for raising the issue; a simple example is needed:

For reasons known only to him, your lunch companion takes out two coins--a quarter and a nickel. He flips both coins—first the quarter, then the nickel. He repeats this five times and, in each case, if the quarter lands heads, so does the nickel; if it lands tails, the nickel also lands tails. "This," he says, "can't be coincidence; the quarter must be forcing how the nickel lands."

Review: XLSim Version 3.2.3

I've been working with XLSim Version 3.2.3 (http://www.probilitech.com) and the news is good.

Odds Are, It's Wrong

It’s science’s dirtiest secret: The “scientific method” of testing hypotheses by statistical analysis stands on a flimsy foundation.

In this article in Science News, Tom Siegfried talks about the misapplication of statistics. His target is scientists who draw statistically-driven conclusions that aren't in fact supported by the data.

It's well worth the read; the lesson goes beyond the purely scientific realm and is applicable to business as well.

Say Yes to Quantitative Risk Analysis

I may be overreaching but I include risk analysis as a proper subject of systems analysis and planning. I've done enough TRAs to justify that position—at least to myself. So here's a risk analysis challenge.