The science of lap dances!

I am impressed with the research. :slight_smile:

I found the link in a NY Times article entitled:
“The Threatening Scent of Fertile Women”

Makes sense. Which makes me suspicious…

For those who can’t access the reference:
Researchers studied the amount of money earned by lap dancers as a function of their fertility. When the dancer was at peak cycle, her earnings peaked as well. Except when they took birth control pills that distorted their cycle. With the pill there was no peak in earnings. The pills are doubly effective!

That’s way too small a study (18 participants) for too short a time (60 days, or about 2 cycles per) and there’s no indication of how the data was collected or info about confounding factors, which could be numerous. So I’d say the results should not be relied upon.

I might be willing to volunteer to replicate. The study, that is.

I should have gotten that science degree after all.

Couldn’t it just mean that they don’t dance as sexily when suffering from PMS and cramping and worrying about if they should have changed their tampon half and hour ago?

Read the article, self-report. I would’ve liked to see some error terms, but otherwise the results are not invalid based upon assumptions. How do you know the sample size is too small? For a mixed-model design, 18 may be more than enough, a better question is "is the sample size much smaller when compared to past studies with similar techniques?

To their credit, they say:

They could just use pheromones like copulins to compensate. They should give those to women and see if it makes a difference, or if other signals are used.

18 participants in a single study isn’t enough to determine anything, especially in a non-controlled, non-double blind study (we don’t know if is was or wasn’t, since they don’t say). Interesting to read, worthless for science.

In my field, most studies are done with fewer than 10 participants. This is very common, and plenty powerful. I don’t know enough about the science of lapdanceology to know what is typical. But with within-subjects and mixed designs, the need to have as many participants is lessened. It sounds like they are not manipulating the contraception use but relying on natural hormone taking. Yes, it can reduce predictive power, but doesn’t automatically invalidate the study.

Did they control for how much alcohol the customers had consumed? (kidding…kind of).

If you have only 10 participants, and you divide those into controls, placebos and tested parties (not sure how you would do it here), then you only have 3 to 4 per group. If one person in one group changes from a Yes to a No, the results are altered by 34 percentage points.

Even if you use 10 per group, if one party changes sides, that’s a 10 percentage point change in the outcome.

No, 10 is insufficient for almost any reliable test if you don’t want to be laughed at.

Here’s a sample size that’s much more reliable:

From here.

I don’t really see any way they could double-blind it, and even single-blinding would be difficult. At best, you could have researchers sampling mucous viscosity, temperature, etc. every day and not telling the dancer what the results where, but the person taking the samples would pretty much have to be in the same room as the subject, and thus there could be information leakage there.

Easy. Both dansor and dansee wear blindfolds. :slight_smile:

Huh? With the pill (not fertile) = no earnings peak.
Ya might want to read up on the various effects of ‘the pill’ (some find help with PMS, cramping, etc).

This seems to be yet another example of very dodgy science.

You take <20 women. They self-select for the type of contraceptive that they use. You then attempt to study the effect of contraceptive. Straight away you have a massive problem: the contraceptive is self-selected.

Think about that for a second. In the 21st century we have young women who are strippers, some of whom are voluntarily not using contraceptives and some who are not. How many reasons can you think of offhand as to why decisions leading to contraceptive choice might affect earnings all by itself. To look at the more obvious:

  1. Women who were not using contraceptives were not in stable relationships. They were either celibate or engaging in causal sex and insisting on condom use. As such they lacked the emotional support structure of a committed relationship and were more prone to umpteen psychological effects.

  2. Women who were not using contraceptives were physiologically infertile, either through surgery, illness or congenital defects. There are way too many ways for this to affect the results to go into here, but to give one obvious factor, most women get tubal ligations after they have already had children, so what effect does having children have all by itself?

  3. Women who are not using contraceptives have more predictable menstrual cycles, less heavy menstrual flows or less severe PMS. Many women use birth control specifically to achieve a more predictable cycle or to alleviate either extreme PMS, especially cramps, or reduce menstrual flow. If this is the case then the women on contraceptives are already behind the 8 ball when it comes to earning.

Looking at the actual paper, the first thing that leaps out at me is how few only a single factor was evaluated.

For example, women not using contraceptives consistently earned more than women who were on all except 3 days a month. That should immediately set alarm bells ringing that the two groups were not selected form the same population. Yet there was not statistical evaluation done to determine if the populations were the same, only an evaluation to see whether phase had any effect on earnings. All that was analysed was whether earning varied between phases *within *groups. No analysis at all on whether it varied *between *groups.

Then we note that while the contraceptive group had a low point of just $75, it peaked at $250, a tripling of income over the cycle. Meanwhile the no-contraceptive group had a low point of $175 and a peak of $375, which is less than a doubling of income. So in fact the contraceptive group had a *greater *increase in earnings over their cycle. Yet no analysis of or discussion of this point is made at all. All that was examined was whether the difference was greater between phases. That is incredibly poor science considering that the studies is supposed to be looking at the effects of cycle phase on earning.

Then we look at the shapes of the curves. The contraceptive group has a nice regular curve, from a low immediately after onset on menstruation to a peak 2 1/2 weeks later followed by a gradual decline. The no-contraceptive group does indeed peak during the fertile cycle, but that peak starts to rise while menstruation is still occurring. Moreover there is a secondary peak in the infertile period in the week prior to menstruation, and that secondary peak is fully 2/3 as great as the putative fertile peak. Yet no analysis was done on the curve forms. No analysis of whether they are the same curve aside form the fertile peak (they clearly are not), no analysis of whether the fertile peak is significantly higher then the infertile peaks.

The only factor that the study looked at is whether contraception use resulted in a greater increase between phases within each group. It seems like a classic example of examining the data for the result that you want, and ignoring the results that are staring you in the face.

Consider me unimpressed. This would be an interesting submission to “Annals of Improbable Research”, but as a serious article, it’s third rate at best.

It doesn’t actually say that. It says that there is no *oestrus *peak.

The data show rather clearly that with the pill there is a peak, and that peak is relatively larger than the oestrus peak for is the no-contraceptive group. It’s just that the peak for the contraceptive group occurs on day 19, about the least fertile day of the cycle.

So, how much do you tip in those situations? I need to know for a science project.

So, what I’m getting here is that more research is needed?

I’ll get right on that.

Very important point. Who says statistics is dull?

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The OP specifically mentions an earnings peak: