Real Life Examples for Conditional Distributions that are not Unimodal I am looking for some examples that Cross Validated community encountered in their work when they are modeling conditional distributions.
So far all the data sets I work with I end up with unimodal conditional distributions.
Can you give some real life examples where you encountered data sets that led you to conditional distributions that are not unimodal?
 A: Saying that there is a conditional multimodal distribution is somewhat like trying to prove a negative. For example, a bimodal distribution is really strong evidence, in most cases, of hidden variable bias. The heights of people is a common bimodal distribution but the hidden variable there is gender. 
In other words, for me to say that a distribution is conditionally multimodal would be to affirmatively state that there was not hidden variable that could be the underlying cause. 
Do multimodal distributions occur naturally?  Yes. Are they conditionally multimodal? Probably not. 
For some phenomena to be naturally bimodal (to use the simplest multimodal distribution) would mean that there would have to exist some threshold x where conditional population y behaves differently upon surpassing that threshold. 
Even in the above lawyer salary, the hidden variable bias is the firm that hires them. In essence, the conditional distribution is unimodal once firm type is controlled for. 
Another example of a multimodal distribution that is only so due to being an unconditional distribution: school district total revenue. Condition on locale (rural, urban, suburban) and you find you have three unimodal distributions.
A: There are many examples:


*

*Here is a post about the price of books for sale on Amazon.com.

*The number of cars that cross the George Washington bridge plotted by the time of day. There will be peaks around 8:00am and 6:00pm, rush hours, with fewer cars in the hours in between.

*Some types of cancer when plotted as a function of age. 

*Also, similar to the traffic on the GW bridge example, on google maps  if you look at a popular restaurant in Manhattan and look at the "how busy the restaurant is" plot which plots by time of day, you usually see 2 or 3 peaks. I've linked one spot that usually has peaks on Saturdays and other days of the week. Some slow days during the week only have 1 peak though. 

*Lawyer's salaries are bimodal. My conjecture is a public sector vs private sector cause of the salary difference. If this is the case you probably see this in other professions too. (this one is taken from comment in OP) 

A: Any experiment that is a mixture of causes with similar effects can have a multimodal conditional distribution. As a simplified case of some of the projects I've worked on, let's say you observe an empty bottle on the shore of a river. You know from the distribution of river speeds (and associated durations) that either (a) the river flows fast for a short time (e.g., storm runoff events) or (b) rather slowly for a long time (dry weather flow), but not much in between. In that case, knowing the bottle was in the water for 24 hours (e.g., by noting the amount of leakage past the cork) will give a roughly bi-modal distribution of possible locations where it could have entered the river (the degree that it actually has two modes depends on the autocorrelation of the river flow regimes and the range of durations for each regime -- the more correlated and longer the duration, the stronger the bi-modal behavior).
