That’s up from 35 million at the end of last year, growth of 1400 percent from 2.5 million at the start of 2009. It’s a dramatic leap, from 300,000 per day in 2008 and 5,000 per day in 2007. In retrospect, it’s funny to think that 2007′s SXSW conference is often cited as Twitter’s tipping point. The numbers just don’t even compare to today.

Twitter has suffered in the public eye when outsiders report its stats. For instance, comScore said last fall the site’s growth was stalling and Nielsen said last spring that most new Twitter users fail to come back the next month. Twitter, whose metrics are especially complicated because so many of its users connect to the service from applications made by outside vendors, could do little to confront or contextualize such stories, given it made so few  of its own stats available.

Kevin Weil of the Twitter analytics team said today that the chart of tweet growth does not include spam, but didn’t give much other detail about how the tweet spread breaks down. We’d wonder how many tweets are from bots, how many from celebrities, how many from what countries, how many from the long tail.

We have to imagine Twitter’s excuses for holding back on bragging about its amazing growth are the fact it only just added its 140th employee — and that the team has often had more important things to deal with, like keeping its service from going down again. I spoke recently with folks from Twitter who said they’re considering making more data available and manipulable for the general public, but such a product has yet to be built. Weil mentioned in his post that tweet deliveries and search are other ways to measure Twitter growth.

Related content from GigaOM Pro:

How Human Users Are Holding Twitter Back

How Will We Access the Next-Gen Web

Though it’s fun to talk about the digital habits of kids as if they were a separate, SMS-crazed species, what happens when teens hit 20? Do they turn the page on their youth and assimilate into adult Internet use? Maybe not. It’s starting to look like online teens may actually have a lot in common with young adults. In two recent surveys by the Pew Internet & American Life Project, many categories of usage saw a high degree in overlap between teens and 18- to 29-year-olds. In fact, it was the over-30 crowd that was the odd man out.

Here are some findings from Pew:

* In the overall U.S. adult population, 74 percent of all adults go online. However, if you narrow that to those aged 18-29, it’s 93 percent. Ages 12-17 — also 93 percent.

* Direct from a joint report write-up on social networking: “Young adults act much like teens in their tendency to use these sites. Fully 72% of online 18-29 year olds use social networking websites, nearly identical to the rate among teens, and significantly higher than the 40% of internet users ages 30 and up who use these sites.”

* While blogging has increased among older adults, it has fallen among 18-29ers. In 2009, 15 percent of them maintained a personal blog, as did 14 percent of online teens. Both the younger demographics have experienced drops in blogging activity in the past few years — which I’d surmise is related to a migration to personal updates on places like Facebook.

* To be sure, there are always going to be fads and trends. For instance, virtual worlds are more popular with young teens, while young adults favor Twitter far more than other groups.

Maybe this is really just about near-native web usage, with the generation under 30 having grown up using the web. If that’s an accurate reading of the data, as young people age into the over-30 category all the usage habits across all ages will start to blend together.

Photo by Flickr user sdminor81.

Related research from GigaOM Pro (sub. req’d):

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Email: The Reports of My Death are Greatly Exaggerated
• Why iPad 2 Will Lead Consumers Into the Post-PC Era
• The Near-Term Evolution of Social Commerce

Indeed, how you count something is as important as what you count. For an excellent example of this in action, look no further than “American Idol.”

To be clear, I am not alleging ballot tampering or conspiracy against certain contestants. Measuring popularity is challenging. But the vote-counting process for “American Idol” has systemic limitations. The results of which are being billed as a precise measure, when in fact, statistically they are not.

This revelation came to me during a recent episode, when I was listening to my 14-year-old daughter lament over how front-runner Adam Lambert was only 1 million votes ahead of the next competitor going into the season 8 finals after over 88 million votes were cast. She was frustrated because she repeatedly hit a busy signal for Adam’s vote line, but she got through more easily for other contestants.

The contest, of course, is not measuring intended votes for each contestant. Rather it’s only counting the votes that are actually make it through the clogged phone lines. A high volume of should-count-but-don’t votes overwhelms the system.

Think of it like a funnel, in which there are different amounts of, say, jelly beans are at the top but only a fixed number of jelly beans make it through to the bottom. In effect, the system is biased to equalize contestant voting to the maximum capacity of the phone lines through which the calls are made. That means that as the number of contestants gets smaller and the vote count gets higher, mathematically, the system biases the results to be close.

For example, say contestant A was extremely popular, and had 10 million calls per minute placed with the aim of voting for him/her. Contestant B was much less popular, receiving just 1 million calls per minute. If the call center could only process and tabulate 1 million calls/minute for each line, then the contestants would have an equal number of votes — contestant A would simply generate an additional 9 million busy signals or rejected calls. Not exactly an accurate representation of their respective popularity.

The system constraints create a statistical artifact of closeness. Further, as the would-be voters for the popular candidate encounter more busy signals, they will be less likely to continue to vote, actually improving the odds of the less popular candidate. By restricting the eligible voting period to two hours following the telecast, it further constrains the system, forcing more and more busy signals — or non-counted votes — into the equation.

A more fair way to count the votes would be to have several phone lines, all of which would give the caller the choice to vote for any of the candidates. Or perhaps an online voting system might be better suited to counting the results, especially since the contest allows multiple votes per person.

Making the results of “American Idol” voting closer than they appear might have been intentional, a bid to boost suspense and ratings. One way or another, the system does not accurately represent voters’ intention. It offers instead a rough estimate with a systemic error — an error that can mean popular contestants can get rejected in favor of less popular ones. It also makes for a terrific lesson in statistics.

Peter Daboll is the CEO of Bunchball. He previously held the Chief of Insights post at Yahoo and prior to that, was president and CEO of comScore Media Metrix.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Why iPad 2 Will Lead Consumers Into the Post-PC Era
• The Near-Term Evolution of Social Commerce
• Content Farms: The Players, The Benefits, The Risks