Doing correct statistics in a working environment? I am not sure where this question belongs to: Cross Validated, or The Workplace. But my question is vaguely related to statistics.
This question (or I guess questions) arose during my working as a "data science intern". I was building this linear regression model and examining the residual plot. I saw clear sign of heteroskedasticity. I remember that heteroskedasticity distorts many test statistics such as confidence interval and t-test. So I used weighted least square, following what I have learned at college. My manager saw that and advised me not to do that because "I was making things complicated", which was not a very convincing reason to me at all.
Another example would be "removing an explanatory variable since its p-value is insignificant". To be, this advice just does not make sense from a logical point of view. According to what I have learned, insignificant p-value could be due to different reasons: chance, using the wrong model, violating the assumptions, etc.
Yet another example is that, I used k-fold cross validation to evaluate my model. According to the result, $CV_{model 1}$ is just way better than $CV_{model 2}$. But we do have a lower $R^2$ for model 1, and the reason has something to do with the intercept. My supervisor, though, seems to prefer model 2 because it has higher $R^2$. His reasons (such as $R^2$ is robust, or cross-validation is machine learning approach, not statistical approach) just do not seem to be convincing enough to change my mind.
As someone who just graduated from college, I am very confused. I am very passionate about applying correct statistics to solve real world problems, but I don't know which of the followings is true:


*

*The statistics I learned by myself is just wrong, so I am just making mistakes.

*There is huge difference between theoretical statistics and building models in companies. And although statistics theory is right, people just don't follow it.

*The manager is not using statistics correctly.


Update at 4/17/2017: I have decided to pursue a Ph.D. in statistics. Thank you all for your reply.
 A: What is described appears like a somewhat bad experience. Nevertheless it should not be something that causes one to immediately question their own educational background nor the statistical judgement of their supervisor/manager.
Yes, very, very likely you are correct to suggest using CV instead of $R^2$ for model selection for example.  But you need to find why this (potentially dodgy) methodology came to be, see how is this hurting the company down the line and then offer solutions for that pain. Nobody wants to use a wrong methodology consciously unless they are reasons to do so.
Saying that something is wrong (which might very well be) and not showing how the mistake affects your actual work, rather than the asymptotic behaviour somewhere in the future, does not mean much. People will be reluctant to accept it; why spend energy to change when everything is (somewhat) working?
Your manager is not necessarily wrong from a business perspective. He is responsible for the statistical as well as the business decisions of your department; those decision do not necessarily coincide always and quite likely do not coincide on short-term deliverables (time constraints are a very important factor in industry data analytics).
My advise is to stick to your (statistical) guns but be open to what people do, be patient with people that might be detached from new statistical practices and offer advice/opinions when asked, grow a thicker skin and learn from your environment. If you are doing the right stuff, this will slowly show, people will want your opinion because they will recognise you can offer solutions where their current work-flow does not. Finally, yeah sure, if after a reasonable amount of time (a couple of months at least) you feel that you are devalued and disrespected just move on.
It goes without saying that now you are in the industry you cannot sit back and think you do not need to hone your Statistics education. Predictive modelling, regression strategies, clustering algorithms just keep evolving. For example, using Gaussian Processes Regression in an industrial setting was close to science fiction 10 years ago; now it can seen almost like an off-the-shelf thing to try.
A: In a nutshell, you're right and he's wrong. The tragedy of data analysis is that a lot of people do it, but only a minority of people do it well, partly due to a weak education in data analysis and partly due to apathy. Turn a critical eye to most any published research article that doesn't have a statistician or a machine-learning expert on the author list and you'll quickly spot such elementary mistakes as interpreting $p$-values as the probability that the null hypothesis is true.
I think the only thing to do, when confronted with this kind of situation, is to carefully explain what's wrong about the wrongheaded practice, with an example or two.
A: Kodiologist is right - you're right, he's wrong. However sadly this is an even more common place problem than what you're encountering. You're actually in an industry that's doing relatively well.
For example, I currently work in a field where specifications on products need to be set. This is nearly always done by monitoring the products/processes in some ways and recording means and std deviations - then using good old $mean + 3*\sigma$.
Now, apart from the fact that this confidence interval is not telling them what they actually need (they need a tolerance interval for that), this is done blindly on parameters that are hovering near some maximum or minimum value (but where the interval won't actually exceed those values). Because Excel will calculate what they need (yes, I said Excel), they set their specs according to that, despite the fact that the parameter is not going to be anywhere near normally distributed. These people have been taught basic statistics, but not q-q plots or such like. One of the biggest problems is that stats will give you a number, even when used inappropriately- so most people don't know when they have done so. 
In other words, the specifications on the vast majority of products, in the vast majority of industries, are nonsense. 
One of the worst examples I have of people blindly following statistics, without understanding, is Cpk use in the automotive industry. One company spent about a year arguing over a product with their supplier, because they thought the supplier could control their product to a level that was simply not possible. They were setting only a maximum spec (no minimum) on a parameter and used Cpk to justify their claim - until it was pointed out that their calculations (when used to set a theoretical minimum level - they didn't want that so had not checked) implied a massive negative value. This, on a parameter that could never go less than 0. Cpk assumes normal, the process didn't give anywhere near normal data. It took a long time to get that to sink in. All that wasted time and money because people didn't understand what they were calculating - and it could have been a lot worse had it not been noticed. This might be a contributing factor to why there are regular recalls in the automotive industry!
I, myself, come from a science background, and, frankly, the statistics teaching in science and engineering is shockingly insufficient. I'd never heard of most of what I need to use now - it's all been self taught and there are (compared to a proper statistician) massive gaps in my knowledge even now. For that reason, I don't begrudge people misusing statistics (I probably still do it regularly), it's poor education. 
So, going back to your original question, it's really not easy. I would agree with Kodiologist's recommendation to try to gently explain these things so the right statistics are used. But, I would add an extra caveat to that and also advise you to pick your battles wisely, for the sake of your career. 
It's unfortunate, but it's a fact that you won't be able to get everyone to do the best statistics every time. Choose to correct them when it really matters to the final overall conclusion (which sometimes means doing things two different ways to check). There are times (e.g. your model 1,2 example) where using the "wrong" way might lead to the same conclusions. Avoid correcting too many people too frequently.
I know that's intellectually frustrating and the world should work differently - sadly it doesn't. To a degree you'll have to learn to judge your battles based on your colleagues' individual personalities. Your (career) goal is to be the expert they go to when they really need help, not the picky person always trying to correct them. And, in fact, if you become that person, that's probably where you'll have the most success getting people to listen and do things the right way. Good luck. 
