# CoolData blog

## 5 August 2010

### Perception vs. reality on the Number 80 bus

Filed under: Planned Giving, Statistics — kevinmacdonell @ 11:26 am

(Photo used under Creative Commons license. Click image for source.)

Do you ride the bus back and forth to work? I do. Some days it’s a quick trip, and other days it just goes on forever. There’s this one stop where the driver will park the bus and just sit there, as the minutes tick by. How dare she. Doesn’t she know I’m in a hurry?

I have some flexibility in office hours, at least during the summer, so I set out to pick the best times to travel. I wanted to know: Which buses on the Number 80 route were most catchable (i.e., had a very predictable time of arrival at the stop closest to my house), were fastest and most reliable (i.e., exhibited the least variability in travel times) and were least full (so I wouldn’t have to stand the whole way).

I was sure that there was some optimal combination of these three, but I couldn’t figure it out just by riding the bus. There didn’t seem to be any discernable pattern to my experience. I did not believe it was random, so there was one conclusion: It’s a data problem.

So I’ve been collecting data on my bus rides, and I’ve just had a look at it. What I found out had less to do with the bus route than with the nature of perceived reality. What you think is going on isn’t necessarily what’s actually happening. (And yes, I’ll bring this back to fundraising.)

I record the time I sit down, and the time I land on the sidewalk at my destination. I note the day of the week (maybe Mondays are quicker rides than Fridays) and the month (maybe buses are less full during the summer months when people are on vacation). I also note how full the bus is (on a scale of 1 to 5), and whether I have to stand (0/1). And finally, I make note of outliers due to “disruptive events” (unusually long construction delays, mechanical failure, etc.)

No one but a geek would do this. But it takes only a few seconds — and if you’re interested in statistics, collecting your own data can be instructive in itself.

I haven’t collected enough data points on the Number 80 bus to reveal all its secrets, but I learned enough to know that I have no sense of elapsed time. Leaving out one extreme outlier, my average trip duration (in either direction) is 38 minutes. So how much do individual trips vary from 38 minutes? Well, 79% of all trips vary from the average by three minutes or less. Three whole minutes! Allow just one more minute of variance, and 90% of trips fit in that window.

All other patterns related to duration are pretty subtle: Late-morning rush hour buses, and the 4:45 p.m. bus tend to have the largest variance from the mean, the first because it’s a quicker trip, the second because it’s longer. The trip home is longer than the morning commute by only about one minute, on average. Tuesdays tend to bring slightly longer trips than any other day of the week — Tuesdays also have the highest average “fullness factor”.

But really, I can hop on any Number 80 bus and expect to get to my destination in 38 minutes, give or take a couple of minutes. That’s a far cry from how I perceive my commuting time: Some quick rides, some unbearably long ones. In fact, they’re all about the same. The bus driver is not trying to drive me crazy by parking the bus in mid-trip; she’s ahead of schedule and needs to readjust so commuters farther down the line don’t miss their bus.

If we can get simple things wrong, think of all the other assumptions we make about complex stuff, assumptions that could be either confirmed or discarded via a little intelligent measuring and analysis. According to what people widely believe about Planned Giving, you can go into your database right now and skim off the top alumni by years of giving and frequency of giving, and call them your top Planned Giving prospects. Your consistent donors are your best prospects, right?

Not necessarily. In fact, in one school’s data, I determined that if all their current, known Planned Giving expectancies were hidden in the database like needles in a haystack, and one were only allowed to use these patterns of past giving to find them again, they would miss two-thirds of them!

We are not wrong to have beliefs about how stuff works, but we are wrong in clinging to beliefs when the answers are waiting there in the data. The point is not that past giving is or isn’t a determinant of Planned Giving potential for your institution — the point is that you can find that out.

1. This post reminded me of book I read a long long time ago called “Are Your Lights On? How to Figure Out what the Problem Really Is”. As I recall, the book has a section on a skyscraper building that was deluged with complaints of long waits for the elevator. The mgt first looked at retrofitting it’s elevator system with faster models, but it would have cost millions. In the end they just mirrored the walls in the elevator waiting area and complaints dropped immediately. Because people looked at themselves in the mirror and straightened ties, adjusted hair, etc their perception of the wait time was drastically changed. I wonder if your bus rides would be faster if they mirrored the inside of the buses? 🙂

Comment by Jeffrey Montgomery — 6 August 2010 @ 2:35 pm

• wi-fi access would probably have the same result.

Comment by artem1s — 10 August 2010 @ 4:02 pm

2. […] my limited powers of concentration, but I read blogs and online media during my commute on the Number 80 bus. I’ve recently discovered Instapaper, which allows me to save plain-text versions of online […]

Pingback by What I’m reading, and how « CoolData blog — 20 December 2010 @ 6:30 am