I’ve claimed that President Obama is following essentially the same policy in the Long War as the prior administration and will continue largely in the same direction.  Of course, I’m an idiot, but wait . . . what’s this?

“Former Vice President Dick Cheney said Wednesday that the Bush administration had developed a new strategy on the war in Afghanistan before leaving office — a strategy that he said “bears a striking resemblance” to the one announced by President Obama in March.”

Now, let’s be careful here.  The Cheney quote only supports part of my thinking – that Obama has been on the Bush page.  The quote doesn’t suggest that Obama will continue which is the more important prediction I’m making.

But, still.

President Obama is in a tight persuasion spot.  Polling doesn’t show a shift in favor of an “Afghan Surge” with the President’s base, but I think that Obama understands the implications of a smallball response to the problems in Afghanistan even if his base doesn’t.  I think he is trying to use an extended public debate as a persuasion tactic to change the position of some in his party.  It is, in essence,  a mass scale dimmer switch that should make more people go High WATT on all the Arguments everyone has to offer on the Long War.

McChrystal’s report seems to have the strongest Arguments in favor of it and all the publicly discussed alternatives, like Vice-President Biden’s ideas for example, tend to produce more challenges and risks by comparison.  Because Obama has dialed up the dimmer switch on this more of his party are thinking more fully and carefully about the different options.

If this is correct, President Obama will start having meetings with leading Democrats who will emerge saying that Democrats in the room were supporting something like the McChrystal report.  There will appropriate to-ing and fro-ing on nuance, subtlety, and all that jazz, but it will mark the beginning of the end of the public debate.

Consider, as an example of this, the presence and role of Senator John Kerry in the negotiations with President Karzai over the run-off election.  Kerry’s actions here make an Afghan Surge more likely, not less likely, because he helped secure a “viable Afghan partner,” a concern raised on Sunday by Mr. Rahm Emanual.  Why use someone like Mr. Kerry for this?  Others could handle the negotiation, but they wouldn’t have the desired gravitas that Mr. Kerry brings.

The largest problem Mr. Obama faces is language or to an extent, branding.  I think that Mr. Obama wants to win big in the Long War – the long term political implications are huge and obvious, not to mention the potential benefits to the West, Afghanistan, and Pakistan – and to do that he must follow much the same policy as Mr. Bush, but he cannot sound like this is what he’s doing as he’s doing it.  If he can find a way to present the Surge in his own terms, in ObamaSpeak, he can bring a lot of his base along.

I think . . .

The most important action in delivering a persuasive play is monitoring or manipulating the receiver’s mental state, WATTage.  You need to understand their Willingness and Ability to Think because this determines whether following Arguments or Cues will operate.  Any proven dimmer switch is of great value to the practical persuader.  Today we look at a surprising WATTage play:  Gearing Up or anticipating future effort.

Tell people they’ll be facing a difficult cognitive task in the future.  Yeah.  To increase current High WATT processing, tell people the future holds a mental challenge.

Research in the Journal of Consumer Research by Anick Bosmans, Rik Pieters, and Hans Baumgartner describes a series of studies that turns the WATTage dimmer switch with making people anticipate a difficult future task.  And, in describing that future difficult task, Bosmans et al., made the participants turn up the dimmer switch on WATTage for a current task.  We call this Gearing Up.

The manipulations themselves are incredibly easy to produce.

Here’s the manipulation from the first experiment:  “In the Easy Future Task condition, participants wrote about a simple and routine task. In the Difficult Future Task condition, they wrote about a difficult and complex task.”

The second experiment:  “Participants were told that they would have to come up with as many gifts as possible that one can buy for €1 for various occasions (birthday, Easter, etc.). In the Easy Future Task condition, the task was described as rather easy (most people had little difficulty coming up with several ideas), whereas in the Difficult Future Task condition, the task was described as difficult (most people found it hard to come up with different ideas).”

Now, the third:  “Participants were told that the first study would investigate participants’ ideas about losing weight and that this would be followed by an anagram study. Future Task Difficulty was manipulated by telling participants that the second study would be a difficult and complex (vs. simple and easy) task and that in an earlier study only 10% (vs. 90%) of the participants were able to recognize half of the anagrams.”

The fourth:  “Future Task Difficulty was manipulated by instructing participants that the upcoming consumer task would be followed by an anagram task that was experienced as either difficult (in the Difficult Future Task condition) or easy (in the Easy Future Task condition) by most participants in an earlier study. Participants received an anagram training task that allowed them to practice the anticipated future task for a time period of 5 minutes.”  (In other words, this manipulation was the same as the Third Experiment, but added the 5 minute practice session.)

Finally, and in the interest of complete description, the fifth experiment was a replication of the Fourth Experiment that added another variable.

Now, how do we know that these anticipations produced immediately higher WATTage?  The researchers had the two groups of participants (Anticipate Hard Effort versus Anticipate Easy Effort) perform the same task.  If the anticipation manipulation had no impact on WATTage, then both groups should perform the same way on the same task.  But, in each study, Bosmans et al., found that the Anticipated Hard Effort participants worked harder on the current task (not the anticipated future task) compared to the Easy Effort participants.

The researcher used two primary tasks to assess the Gearing Up manipulation.

Here’s how they described the first task.  “In this focal task, participants evaluated a new (fictitious) diet drink. The new product was presented on a computer screen, and participants were instructed to consult as much information as necessary to arrive at a good evaluation of the new product (adapted from Martin et al. 1993). They could do so by clicking on several “links” (eight in total) that contained information about ingredients and manufacturer and consumer testimonials. Participants were instructed to click the “OK” button when they felt they had sufficient information to evaluate the product. The number of information links consulted was the measure of effort allocation.”

For the second task:  “Imagine the following scenario: 1 month from now, you are invited to a high school reunion. You haven’t seen your classmates for years, and you want to make a good impression. One of your goals is to lose 6 pounds of weight by the time you meet your former classmates.  In the space below, please describe in as much detail as possible how you could lose 6 pounds in a time span of 1 month. Please list as many ideas as possible that would enable you to do so.”

Now, we’re down to the proof.  What about the effect sizes!?!  The effect sizes ranged around a medium (35/65) Windowpane effect.  It appeared that when the task required more action (writing down dieting ideas) the effect sizes were lower and when the task was easier (clicking on product attributes linked on a computer screen) the effect sizes were higher.  Without specific experimental testing this is just eyeballing the results, always a tricky analysis.

Thus, when a source says, “Gear Up,” or when a source has a receiver practice Gearing Up, the act of anticipation for future effort will increase WATTage immediately and apply to the next task at hand.  Consider practical applications.  You could use Gear Up aimed at a task the receiver finds interesting or enjoyable then switch their WATTage to an immediate task that may be less interesting or desirable.  If you’re a parent you should see obvious applications.  Same with anyone in a leadership or supervisory role.  I can also see f2f sales applications where you tell a customer about a product or service that we’ll talk about in a few minutes and that “You really need to put on your thinking cap for that one!”  Then move the conversation to the immediate Arguments you wish to make.

And, Argument processing is the Main Point with Gear Up.  If you don’t have strong Arguments, don’t Gear Up.  Right?  If you know that you’ve got the killer app, product, service, idea, whatever, then use Gear Up to move your receiver to High WATT.

Let’s now recall a recent post on Drano, the Depletion dimmer play.  If you need to refresh your memory or get up to speed you can check it out, but quickly here:  the Drano play has people perform a task that requires cognitive effort now which is used to turn DOWN the switch to produce Low WATT Peripheral Route processing of a later Cue.  This sounds a bit like Gearing Up, but with very different functions and results.  But, catch the difference.

I think that Drano requires much more difficult tasks than Gearing Up.  Here are a couple of Drano tasks:

Cross out every occurrence of the letter “e” in a page of dense text taken from a statistics text book, then do the task again, but this time cross out the letter “e” only when it follows a vowel in a new page of dense text from a statistics text book.  (Control did first task and repeated it, thus not learning the new “e” rule.)

Complete a  4 minute geometric figure tracing task with your nondominant hand while looking at the figure in a mirror. (Control did this with their dominant hand without the mirror.)

Now, contrast with the Gear Up tasks.  Some participants were merely told that a later task would be more difficult or that they would be doing anagrams again merely described as “hard.”  Only in one experiment did participants actually do a cognitive task (practicing anagrams for 5 minutes).

Without direct experimental testing, I would argue that Drano needs a much stronger manipulation than Gear Up, that is participants must actually complete a difficult task with Drano.  Furthermore, you can manipulate Gear Up by simply saying the future task will be difficult whereas Drano again requires actual completion of a task.

I think . . .

Let’s hit the Outro.

Gear Up is a dimmer switch play, a specific set of words and actions you as a persuasion source produce to affect the WATTage of your receiver.  You employ Gear Up when you’ve got a Low WATT receiver AND you have strong Arguments.  If the receiver is already High WATT, you obviously don’t need Gear Up and if you have weak Arguments, you definitely don’t want to Gear Up.

And, real nice work from Anick Bosmans, Rik Pieters, and Hans Baumgartner.  If you are stone cold crazy for experimental persuasion science, this is a great exemplar.  Clear conceptualization and rationale for Gear Up, a smart set of interlocking experiments that test relationships, moderators, mediators, and effects.  I admire the effort and skill here.  Good science is really quite easy if you’re willing to die for it.

On Sunday, Rahm Emanuel asserts the US can’t do anything if Afghanistan has a corrupt government.

On Tuesday, Karzai announces he’s down with the findings of the Election Commission and is ready for a November 7 run-off election.

Yeah, Obama’s gonna go light in Afghanistan.  Drones.  CSI Kabul.  Maybe even bring the troops home from Christmas.

Or.

Karzai was sitting at home on Sunday watching Rahm on the talk shows and thought, “Hmmm, maybe he’s right.”

Or.

Obama’s going long on the Long War, folks.

He’s got to bring his base on board.

Play nice.  Help him.  Ask for Pelosi’s opinion.  Or Byrd’s.  Listen empathetically.  Stir in a Stimulus Project.  Get one Republican vote on health care reform.  Even if it fails.

He cannot, not.

I  think . . .

I’ve seen this before.  You bust your tail to get the resources for a Really Big Test, then get the results back.  And they stink.  What do you do?  How do you report this?  Straight up with nothing but heart breaking science?  Nah.  You use a little persuasive writing.

Read this study about a new HIV/AIDS vaccine test reported in the New England Journal of Medicine.  That report discusses results from a large, properly conducted randomized clinical trial where study volunteers with no measurable signs of HIV/AIDS infection are given a shot that either contains the vaccine or a placebo.  All volunteers who properly complete the treatment (real or placebo) are then measured every six months for three years.

Here are the primary results.

6366, 50
6176, 36

6366 people got the placebo shot series.  And after three years, of these 6366 people, 50 got infected with HIV/AIDS.  In the vaccine group, of 6176 people, 36 people got HIV/AIDS.  Hey, 36 (vaccine) is less than 50 (placebo), right?  It works, right?

Well, sure one number is higher than the other, but is the vaccine the ONLY possible cause?  We did randomly assign people to vaccine or placebo.  Could that make a difference?  Think about it.

Imagine you have a red die and a white die.  You make a machine that tosses dice fairly and records the results of each toss.  You toss the red die 6366 times and the white die 6176 times and compute the mean score for the series.  You will find that the red mean and the white mean will be close, but not identical.  And, if you do this silly experiment hundreds of times you will find a wide range of difference between the red mean and the white mean.  And, the only cause for the variation is the random sampling process itself.  This is the essence of testing for statistical significance.  Merely by random sampling of events, there will be variation.

Now, the good news is that it is very easy to do the math on this.  We understand this sampling variability quite well nowadays (does the term, Bell Curve, ring familiar?).  And, we realize that in a randomized controlled trial of anything, vaccines, surgical procedures, or the persuasion WAC, there is always a rival explanation to our results.  Sure, maybe a difference was caused by the vaccine, the new surgery procedure, or the WAC, but it’s also possible for the difference to arise merely by sampling variation.

So, let’s run the test of statistical significance on our obtained numbers.  We find that .0078% of placebo people got HIV/AIDS versus .0058% vaccine people.

Blink.

Look at those percentages.  One percent would be written as 1.0000%.  So, .0078 and .0058 are considerably less than one percent of the study participants who got HIV/AIDS over a three year time period.  You could call that a low frequency event, couldn’t you?

But, keep your focus.  We’ve got a difference:  .0078% versus .0058%.  That’s a real quantitative difference.  But did it arise from the vaccine or from sampling variation?

You do the appropriate test of statistical significance and it says this:  p = .16.

What that means is if we did our dice tossing experiment 100 times, 16 times we’d get this kind of difference or larger, just by sampling variation alone.

If you’ve got training in statistics and research methods, you know that the “traditional” cut off point is p < .05 to call something “statistically significant.”  Researchers have come to interpret obtained differences that could arise only 5 times or less as being important, useful, reliable.  Why the 5% criterion has become the industry standard is like many things, just a convention, a tradition that grew up over time.  It seems rare enough to discount and instead claim that our obtained difference is due to the vaccine, surgery, or WAC and not sampling variation.

Thus, using the long-held convention of 5% as our interpretive guide, our 16% probability means that either the vaccine or mere sampling could have produced the difference of .0078% versus .0058%.

Which means by the standards of science that are over 100 years old, the damn vaccine DIDN’T WORK.  That the vaccine “caused” a result that can just as easily be explained by sampling variation.  That we’ve got a reasonable rival explanation for our experimental outcome and being conservative, traditional scientists, we retain the null hypothesis and reject the research hypothesis.  That’s the standard.

How do you put lipstick on this pig?  How about this scientific persuasion:

“. . . the results show only a modest benefit . . .”

” . . . there was a trend toward prevention of infection with the vaccine regimen.”

“However, after the exclusion of the subjects who were infected with HIV-1 before vaccination, the modified intention-to-treat analysis showed a significant, though modest, reduction in the rate of HIV-1 infection, as compared with placebo.”

“Taken together, these data are consistent with a modest protective effect of vaccine in this study.”

Quotes from study authors led by Supachai Rerks-Ngarm, M.D.

“In this issue of the Journal, Rerks-Ngarm et al. report the results of a clinical trial of a vaccine regimen (ClinicalTrials.gov number, NCT00223080 [ClinicalTrials.gov] ), describing the first findings of possible prevention of HIV-1 infection in humans. This is of potentially great importance to the field of HIV research.”

“Although the merits of each type of analysis can be debated, all three yielded a possible, albeit modest, effect of the vaccine in preventing HIV infection.”

Quotes from editorialist Raphael Dolin, M.D.

A “modest” effect?  MODEST?  What is a “modest” effect?  Where’s the text book that claims: “By convention when obtained results fail to reach the traditional .05 alpha level of statistical significance and instead produce an outcome of .15 alpha, we will call the outcome, ‘modest,’ rather than ‘*$%#%@*$!&$#’ which is what we say looking at the printout.”

This is a stone cold failure.  It didn’t work.  It had no effect.  It didn’t affect important biological indicators like CD4 counts.  Sure, you can call it modest, unassuming, diffident, slight, slender, or slim, but the quantitative outcome is a failure.

And, now, remember that blink when you realized how small the infection rate was, less than one percent, way less, anywhere between .005 and .007.  These are minuscule fractions, tiny decimals, itsy-bitsy numbers.  The researchers needed over 12,000 cases in this study to even score something this little and still call it “modest” with a straight face.  Do you know how much time and money you need to recruit, inject, measure, and follow 12,000 people?  Millions and millions of dollars.  And, it still wasn’t enough to produce a statistically significant result.  A “modest” result, though.

These are nice rhetorical tactics that obscure the science, the numbers, the real pain.  Using a term like “modest” blurs the failure and makes it sound like something good, predicted, and explainable happened.

And, if you are really rhetorically savvy, you’ll put out a PR release on this and generate favorable coverage in the popular media.  Like this.

Anyone who thinks that science doesn’t use persuasion will never get tenure, a grant, or a publication.

Today’s demonstration of persuasion science focuses upon persuasion campaigns aimed at changing large groups of people in natural, nonlaboratory setting – in other words, the real world.  I’ll spotlight research I’ve worked on because I know it well, it survived peer review for publication in scientific sources, and it has been cited by other researchers who don’t know me from Adam, Eve, or the serpent.  It seems to work.

A persuasion campaign is a designed set of messages targeted at a group of receivers living in their natural world aimed at producing changes in thoughts, feelings, and actions a small group of sources wishes to obtain.  Persuasion campaigns compete in the natural marketplace of information and use the resources available to other public communicators (politics and government, advertising and sales, public relations, etc.) and compete for the attention of the same citizens.  Persuasion campaigns are part of the noisy free speech discourse that forms the framework for American democracy.

Now, do persuasion campaigns actually work?  The common sense answer is, well, since people do them they must work, right?  That kind of logic drove Wall Street of its most recent cliff, so you realize that simply because people do it, doesn’t mean it works.  Lots of people do marriage and a lot of them fail, too.  We need to get more systematic to understand this.

Leslie Snyder conducted one of the best summaries of persuasion campaigns focused on health and safety behaviors.  In particular she did a meta-analysis which means each campaign reported real data that actually measured change.  At the time she published this (2002) she and her team had located 47 comparisons, published in peer review journals.

From those 47 tests Snyder et al. reported an average change, expressed as a correlation, of .09.  In Windowpane terms this corresponds to an approximate 45/55 effect, a “small” effect size.  And, in this instance, the correlation also corresponds to the absolute percentage difference between treatment and control, thus we have about 9% improvement on average.

Let’s just think about this for a minute.

First, the effect is positive.  Persuasion interventions can produce desired change in natural populations living normal life.  Second, the effect is not staggering, obvious, huge, Holy Mackerel, but rather is small, meaning it is detectable above the normal variation of living things, but you need a statistician to find it (or invent it!).  Persuasion campaigns are not Magic Bullets.  Third, since the average is about 45/55, that means some interventions produced larger effects and other smaller.  What about that variation?

For me, this is the crucial insight you have to make if you want to understand how to do persuasion campaigns.  The “average” campaign produces a small effect which means if you run a campaign you can make a difference, but there is variation around this average which means if you run your campaign badly, you can have worse outcomes.  Let’s look at a table that displays the variation in those 47 tests.

Get oriented.  The “Effect Size” column offers categories of effects (expressed as the Pearson correlation) that are .05 wide.  The “N” column shows how many tests fit in that category.  Thus, there were 17 tests that showed effect sizes between .00 (zero, right?) and .05.  There were 5 tests between .16 and .20.

In looking at this, the first thing I observe is that is it not anything remotely approaching a normal distribution with that lovely bell shaped curve showing the fat middle and skinny tails.  Look, 31 (17+14) of the 47 tests are less than the average!  By contrast, only 16 (8+5+3) are above average.  Thus, most campaigns have effects that are functionally zero and certainly less than small.  Yet, about one third of the campaigns are better than average.  Thus, while the “average” effect is small, two thirds of campaigns don’t hit it.  And, if you want to be real grumpy about this only the top 8 tests seem to demonstrate powerful interventions; that’s a success rate of 17%.  As an executive summary, the headline here is not real encouraging:  Sure the “average” is small which means you actually change things, but most campaigns don’t hit that and if you want serious, obvious change you’ve got less than a 1 in 5 chance of succeeding.

How do you hit that higher success rate?  My own experience with doing these things and my reading of the research literature leads me to what I call the Standard Model.  Let’s look at one set of studies I did on using persuasion campaigns to change nutrition behavior.  It’s important to consider the set rather than any one paper, study, or finding because the set reveals the pattern of the Standard Model.  (Read more about it on the Primer pages SM1, SM2, and SM3.)

You can read the gory details in the publications, but for this post, please take my word for it.  We wanted people to switch from high fat milk to low fat milk (1% or Less!).  Across highly similar West Virginia towns, we ran four tests of a treatment versus a control.  The treatment and control towns were far away from each other and outside of each other’s (small) media markets.  We also drew random samples of people in each community for pre and post testing, so we had a quasi-experimental, pre-post design, repeated four times.

In each treatment, the “persuasive message” was always the same (based on extensive message testing also called formative research).  What we varied was the channel that carried the message.  We created three types:  Ads, PR, and Education.  Ads were purchased and clearly identified as advertising placed in local TV, radio, and papers.  PR comprised events we staged to draw free local news coverage in TV, radio, and papers.  Education was the standard face-to-face community organizing at meetings, churches, associations, etc.  Here’s how the treatment towns got different combinations of channels.

Treatment 1 (paid ads, PR, and community education)
Treatment 2 (paid ads and PR)
Treatment 3 (PR and education)
Treatment 4 (paid ads)

The key behavior outcome was switching.  At pretest we asked participants in treatment or control to tell us what kind of milk they used.  At posttest we asked the same participants the same question and computed “switching” as the difference between pre and post.  (This is one helluva difficult way to measure switching.  Most folks would dispense with the pretest and just ask at posttest whether you’d switched in the past couple of weeks.  If you don’t see the difference between defining switching as our pre post test versus just asking, you are not a good researcher and will someday face embarassing public proof of it.)

Let’s get to the results.

Remember the Snyder meta found an average 9% improvement.  Across our 4 tests, our average improvement was 19.4%, twice as large as the meta average.  And, more interestingly, there is clear variation in switching depending upon the combination of channels.  The combination of ads and PR produced a nearly 30% improvement compared to the other two combinations that produced an average 9% improvement.

And, if you look at the last column, Reception Rate, you see why.  We got more reception with the paid/PR combination than with the others.  Thus, the Standard Model produced average results that were twice as good (19% versus 9%) as the meta and we could enhance that advantage to a tripling (30% versus 9%) through smart Reception planning.

Of course there’s a lot more going on here.

With Treatment 1 we also collected supermarket milk sales data at three times:  pre, post, and six months later.  Control milk sales did not vary practically or statistically over the 3 periods.  Treatment sales showed a big increase pre to post in low fat milk sales (yeah, baby) that were maintained six months after we ended the campaign (hell yeah, baby).  This sales data effect is exactly what you’d expect if people took the Central Route to attitude change based on our campaign messages.  Central Route change produces effects that persist over time and that’s what ths sales data at six months demonstrate.

Also with Treatment 1 we collected full Standard Model data (Reception to Processing to Response to Behavior) and analyzed it with structural equation models (path analysis).  Our message testing indicated that people’s decision to switch was driven by their Attitude (cost, taste, enjoyment) and not their Norms (what other people think they should do).  As a result, our campaign messages offerred strong Arguments that focused on Atttiude (e.g. low fat doesn’t cost any more than whole), but no Norm Arguments (your friends drink it and you should, too).  So, we expected a path model to show that the Treatment changed Attitude (not Norm) which changed Intention which changed Behavior.

Here’s the model with path regression weights.

-                          — .31 –>  Attitude – .48 –>
Treatment                                                               Intention — .56 –>  Behavior
-                         — .0 –>   SubNorm — .0  –>

Exactly what we predicted.  The treatment messages changed Attitude (regression weight of .31) but not Norm (.0).  Then Attitude changed Intention (.48) with no effect from Norm.  Finally, Intention changed Behavior (.56).  The overall model (MR = .71) explained over 50% of the variance and had a Goodness of Fit index of .999.

Here’s another way to display the Response change.

There was a “large” difference in Intention to switch (3.2 versus 2.2), a “medium” difference in Attitude (41.8 versus 35.5) and a “small” difference in Norm.  In most published research, that small Norm difference would be the biggest effect the campaign would produce, but with this Standard Model, it is a piddling effect that is largely due to the strength of the other variables.  In other words, if you dropped Intention and Attitude from the analysis and just looked at Norm, it would not even be statistically significant.

Okay, there’s a lot going on in this post and a lot of it is that Stat Geek Speak.  Like I mentioned earlier, you can read the details, but for now you have to take my word that I’m accurately reporting the findings.  I can understand your suspicion and frankly, I strongly encourage it.  Read the original reports.

Now, what’s the practical payoff with all this mumbo jumbo.

First, assuming it is an accurate reflection of the literature, the Standard Model has the strongest proof of life available.  It works and we know why it works.

Second, structured, planned, and careful persuasion produces powerful practical change.  If you know what you are doing, the Standard Model makes you dangerous.  You can change freely choosing people in their natural environment.  You don’t need tricks or games or power.  Just smart, well structured persuasion.

Third, the Model not only helps planning and execution, but it really helps for evaluation and modification.  When you get “bad” results from an intervention, you need to figure out why you are failing.  The Standard Model tells you not only what to do, but explains what is going wrong if something does go wrong.  Are you really generating Reception?  Are those really strong Arguments or are you kidding yourself?  The Model provides contours and will show you the cliff you just fell off.  Of course, you’re hitting the ground right now, but you know you’ve got a cliff, why it’s there, and what to do to handle it in the future.

Fourth, never forget that the Standard Model applies persuasion to behavior change problems.  This is not a marketing, advertising, or macroeconomics model.  It aims at changing individual behavior, one person at a time.

Finally, if you think you’ve got something better than the Standard Model, that’s fine with me.  But, if you can’t join it, then you should beat it.  And, I’d like to see those numbers, please.