For the 99th T-SQL Tuesday, I’m going to talk about something I do that’s completely unrelated to work. Something I’ve never told any of you about.

What I don’t want you to know

There are 3 things in my life that could cost me a job, things that I fear an employer ever finding out about. I suffer from 3 big problems in life: depression, diabetes, and LARP. That’s right, I’m… a LARPer.

I’m not kidding about the fear.  People get weirded out easily, and it’s easier to say no to an applicant than to say yes. I really worry about someone finding this post and turning me down.

I once heard a story about a potential staffed employee. Everything was looking good until the client looked up the employee’s Facebook and found out that they were a furry. After that point, the client was no longer interested in staffing that employee.

It’s unfortunate that harmless personal hobbies present such a risk, but here we are.

How it started

When I first heard about it, I was skeptical. In my head, all I knew was the stereotype of the uber-nerd shouting MAGIC MISSILE!

My then fiancée and a couple friends of ours had started going, and she wanted me to come along. I told her quite clearly, “Listen, LARP is so dorky that if I go with YOU, you’ll end up breaking up with ME. No way.”

Well it turned out that my excuse had a limited shelf-life. A couple days after we are married, my new wife reminds me that I’m very much against us getting divorced. So now I have no excuse not to go, because we aren’t breaking up any time soon. We go to LARP a week after we are married.

What is it?

I could tell you it’s Dungeons and Dragons in the woods, but that would be doing it a disservice. Instead, let me tell you a couple stories from Memorial Day weekend, 2016. It was 8 months later, and the start of our honeymoon.

That time I tried to commandeer a flying ship

So my character is a diplomat / minstrel / healer. Which means, if possible, I’d prefer to solve things with words instead of violence. And one of my abilities is to use my charisma to persuade people, convince them we are friends, etc.

So, me and my group are informed by a Non-Player Character that some bandits have taken over his grounded airship, and he needs us to get it back.

We approach the ship, and as we get on the ramp the bandits warn us to stay back. I tell my group, “Let me handle this.”

So I walk up the ramp, alone and in mild danger.
I tell the bandits, “Hey, I’m here to help. That guy you stole this ship from, he’s trying to find adventurers to kill you.”
“We didn’t steal it!”, they reply.
“Fine, the former owner is looking for you.”, I say as I keep walking up the ramp.
”Stay back!” they shout. Then I throw two spell packets at them. Spell packets are basically birdseed wrapped in cloth. This allows for ranged attacks without hurting us or the wild life.

Now the two bandits at the front are charmed and best friends with me. They are trying to convince the rest of the bandits that I’m legit. That they know me from…somewhere?

“Now, here’s how I’m going to help you.” I say as I lean forward, at the top of the ramp. As my foot hits the ground, I hear a loud squeak.

Crap.

“Hoooooold!”, we shout. We have to pause the game because something important just happened. I stepped on a trap. An obvious trap. A trap so obvious that the person setting them up expected everyone to see it. Nope, not me. I’m too busy with my silver tongue.

“10 fire damage in a 10 foot radius. That counts as an aggressive action and breaks charisma.”

Crap. I guess we aren’t BFFs anymore.

I try to re-charisma the one bandit and convince her that she set off the trap. She apologizes profusely, but the rest of the bandits are not convinced and a melee ensues.

Well, at least I tried. Now we have to kill them all. This is what happens when you travel with murderhobos.

Thankfully, all of the dead bandits are very forgiving.

That time my hat was a cauldron, and it saved the swamp

“Keshan, do you know how to featherfall?” I’m asked.
“Sure, I just learned it.” I reply.
“Okay, we need you to climb this tree [metaphorically] with these magic flowers, and then jump out of the tree.”, Angus says.
“No problem.”

I jump and do a twirl and pretend I’m falling out of a tree. Now they can make some magic potion that’s going to cure the swamp of some ancient curse.

Only there is  a problem. “Where is the cauldron?” says the game marshal, “You can’t make a potion without a cauldron.”

Mind you, we are preparing for a town fight, a battle between all of the players and all of the NPCs. As far as our characters are concerned, we could be attacked at any moment.

So I shout, “There’s no time! Zanrick, turn my hat into a cauldron.” Zanrick here is a gnome, and they are a crafty folk. And they have an ability called improvise, which means they can make something into something else if they B.S. well enough.

More shouts go out, “We need an amour patch!”. We end up with 3 of them. Technically they are mason jar lids, but in game they are used to repair your armor after a fight. And suddenly, my hat is a cauldron brewing a magic potion to save the swamp. Pretty cool, right?

The best part of all of it? The game marshal was so delighted by my silliness, that my hat now counts as plate armor in-game. She was just expecting one of us to go to the kitchen and grab a pot. Well, that simply wouldn’t do.

To hell with what everyone thinks

There was a recent conversation on Twitter about how when you are 40, you learn to worry less about what other people think, and you learn to do what you love. I’m not quite 30, and I worry what people will think of me, but damnit I love LARP.

I mentioned depression at the beginning of this post. I usually don’t like to talk about it, because I don’t want to seem like I’m fishing for sympathy. But it’s worth mentioning that one of the causes of depression is ruminating or obsessing over negative thoughts.

In my life there have been only 4 things that have truly gotten me out of my own head and allowed me to stop worrying about things:

1. Video game programming competitions
2. Board games
3. First-person shooters
4. LARP

That’s it. Those are the only things that allow me to escape for a little while.

But even more than that, LARP is an excuse to turn off my cell phone. It’s an excuse to go outside and walk around. It’s a situation where I’m forced to be hyper-social and meet new friends. It’s this beautiful mathematical dual to everything I do in IT.

I do it to get away. I do it get a break. I do it to make friends.

As I get older, all of those things get harder and harder to do. I may take my work home with me, but there’s at least one place in this world where my work can’t follow me.

One last thing

One last thing, that I should probably mention. So, I might have published a book, in-game. Because why the hell not.

Last year I got to do a Precon on Power BI for the Pittsburgh SQL Saturday. This year I’m honored to be presenting at Cleveland and Cincinnati. This time I thought it would make sense to have a blog post summarizing what’s covered.

One of the things that I found frustrating when I first learned about Power BI, was all of the behind the scenes stuff. It was easy to find information about charts and graphs, but less so about how everything fits together. This precon focuses on two main areas: data wrangling, and administration.

Session 1: Database Theory

Because Power BI is aimed at business users in large part, there are many people using it who don’t have a traditional data background. This means it’s worth touching on some of the fundamentals such as primary keys, normalization and star schema.

The most important things to understand when modeling for Power BI, is that it’s optimized for star schema in particular and filtering/aggregating in general. That fact that it’s a columnar database means it can handle a certain amount of flattening/denormalizing gracefully, because it has really good compression.

Session 2: Power Query

One of the things that can be confusing is that it has 2 different data manipulation languages, M and DAX. (3 languages if you could R!). So a question that comes up a lot is when to use which language.

Power Query is designed for business users primarily, especially since it started as an Excel add-in. In fact the official Microsoft litmus test is that is was designed for users who get value from the excel formula bar in their work. As a result, it has a strong GUI component, but is really basic in a lot of ways.

The way I like to think of it is “Anything you could pay someone minimum wage to do in Excel, you can automate in Power Query.” Power query is all about basic clean up and data prep. You aren’t going to be adding a lot of meaning to the data.

Session 3: DAX

DAX is the language you are going to use to model your data and add meaning to it. DAX is deceptively simple, looking very similar to Excel formulas. In reality, the learning curve on DAX can be quite painful, because it requires thinking in terms of columns and filters, not in terms of rows.

Session 4: Data Gateways

Data gateways are the way that you bridge the cloud Power BI service to whatever data lives on premises. Installation and configuration is pretty simple overall. Data Gateways allow for schedule refreshes of your data up to the cloud.

One thing that’s worth knowing are the alternative query methods available it gateways. By using DirectQuery or live connections, you can query live data without having to export it all to the cloud.

Related course: Leveraging Timely On-premises Data with Power BI

Session 5: Licensing and deployment

With power BI, generally you are going to be buying pro licenses for all of your users, at $10 per month. However there are other licensing scenarios such as Power BI Reporting server and Power BI Premium. But you are probably going to be going with the pro license.

There are so many was to publish Power BI reports:

1. Personal workspaces
2. App Workspaces
3. Organizational content packs
4. Publish to web
5. Sharepoint
6. Power BI Premium
7. Power BI Embedded
8. Power BI report server

It can get a bit difficult to keep up with all of the options.

Session 6: Security and Auditing

There are three big pieces to securing Power BI: What data can be access, what reports can be accessed and what can people share. In addition to that, there are interesting features with row-level security built in to Power BI as well as SSAS.

In terms of auditing, much of that is going to be based on the Unified Audit log for Office 365, which requires some work to enable. There are also things you can do with PowerShell and with auditing data gateways.

Overview

Overall I’m pretty proud of the contents. This is the kind of precon I wish I had been able to attend 3 years ago so I had an idea of what I was doing.

I’m planning on building a salary calculator based on the data from played around with some of the numbers earlier. This time I’m planning on going a lot deeper.

I want your help and feedback! I want to know what would make a calculator most useful to you. Feel free to poke holes in my methodology and tell me how a real data scientist would handle this project.

In this post, I’m going to outline some initial findings as well how I’m planning to approach this project. All of the information below is based on a narrow subset:

• USA, full postal code
• DBA job
• Between $15,000 and $165,000

Regarding zip codes, some people only entered a portion of their zip for privacy sake. In the final analysis, I plan on taking into account the ~200 US individuals who did that.

Initial findings

The data isn’t very predictive

So I’m using something called a multiple linear regression to make a formula to predict your salary based on specific variables. Unfortunately, the highest Coefficient of Determination (or R²) I’ve been able to get is 0.37. Which means, as far as I understand it, that at most the model explains 37% of the variation.

Additionally the spread on the results isn’t great either. The standard deviation, a measure of spread, is about $25,000 on the original subset of data. Which means we’d expect 68% to be within +/- $25,000 of the average and 95% to be within +/- $50,000 of the average. So what happens when we apply our model?

When we apply the model we get something called residuals, which are basically the difference between what we predicted and what the actual salary was. The standard deviation on those residuals is $20,000. Which means that our confidence range is going to be +/- 20-40k. That to me doesn’t seem like a great range.

There are a few strong indicators

Let’s take a look at what we get when we do a multiple regression with the Excel Analysis ToolPak addin:

The two biggest factors by far seem to be how long you’ve worked and and where you live. In fact, we can explain 30% of the variance using those two variables:

The two other variables that are very strong are whether you telecommute and whether you are independent. When we add those, our adjusted R² goes up to 33%.

Then after that we have a handful of variables that have a less than 5% chance of being erroneous:

• Gender. It’s still a bit early to jump to conclusions, but it looks like being female might cost you $6,000 per year. This is after controlling for years of experience, education, hours worked, and if this is your first job. Gender could still be tied to other factors like a gap in your career or if you negotiate pay raises.
• First Job. “First job” I identified as having identical values for years of experience and years in this job. If you haven’t changed jobs, it could be costing you $4,000, which lines up with my personal experience.
• Hours worked per week. This is basically what you would expect.
• Education. This is the number of years of education you received outside of high school.
• Build Scripts and automation. One of the tasks people could check was if they are automating their work. Out of all the tasks people could list, this seems to have the biggest impact.

There are some interesting correlations

Part of doing a multiple regression is making sure your variables aren’t too strongly correlated or “collinear”. As part of this, is possible to find some interesting correlations.

• If you are on-call, you are less likely to have post-secondary education. You are also probably overworked and learning PowerShell (no surprise there).
• Certifications correlate negatively with being a dev-dba instead of a production dba.
• If this is your first job, you are less likely to be working more than 40 hours per week. Maybe that $4,000 paycut is worth it
• Independents also work less hours per week. So maybe your second job should be going independent.
• If you telecommute, you might make $2,000 more per year for every day of the week you telecommute; but you are going to be working more hours as well.

Plans moving forward

So here is the current outline for this blog series:

1. Identifying features (variables)
2. Data cleanup
3. Extracting features
4. Removing collinear features
5. Performing multiple regression
6. Coding a calculator in Javascript
7. Reimplementing everything in R

So let me know what you think. I plan on making all of the data and code freely available on github.

Brent Ozar recently ran a salary survey for people working with databases, and posted an article: Female DBAs Make Less Money. Why?

Many of the responses were along the lines of “Well, duh.” I, personally,  felt much of the same thing.

But, I think with something like this, there is a risk for confirmation bias. If you already believed that women were underpaid, there is a chance that you’ll see this as more proof and move on, without ever questioning the quality of the data.

What I want to do is try to take a shot at answering the question: How strong is this evidence? Does this move the conversation forward, or is it junk data?

Consider this blog post an introduction into some statistics and working with data in general. I want to walk you through some of the analysis you can do once you start learning a little bit of statistics. This post is going to talk a lot more about how we get to an answer instead of what the answer is.

Data Integrity

So, first we want to ask: Is the data any good? Does it fit the model we would expect? Barring any other information I would expect it to look similar to a normal distribution. A lot of people around the center, with roughly even tails on either side, clustered reasonably closely.

So if we just take a histogram of the raw data, what do we get?

So, we’ve got a bit of a problem here. The bulk of the data does look like a normal distribution, or something close to it. But we’ve got some suspicious outliers. First we have some people allegedly making over a million dollars in salary. That’s why the histogram is so wide. Hold on, let me zoom in.

Even ignoring the millionaires, we have a number of people reporting over half a million dollars per year in salary.

We’ve also got issues on the other end of the spectrum. Apparently there is someone in Canada who is working as an unpaid intern:

Is this really an issue?

Goofy outliers are an issue, but the larger the dataset the smaller the issue. If Bill Gates walks into a bar, the average wealth in the bar goes up by a billion. If he walks into a football stadium, everyone gets a million dollar raise.

One way of looking at the issue is to compare the median to the mean. The median is the salary smack dab in the middle, whereas mean is what we normally think of when we think of average.

The median doesn’t care where Bill Gates is, but the mean is sensitive to outliers. If we compare the two, that should give us an idea if we have too much skew in either direction.

So, if we take all of the raw data we get a $4,000 difference. That feels significant, but could just be the way is naturally skewed. Maybe all the entry level jobs are around the same, but the size of pay raises get bigger and bigger at the top end.

Averages after removing outliers

Okay, well lets take those outliers out. We are going to use $15,000-$165,000 as a valid range for salaries. Later on I’ll explain where I got that range.

There are 143 entries outside of that range, or about 5% of the total entries. I feel comfortable excluding that amount. So what’s the difference now?

So the middle hasn’t moved, but the mean is about the same now. So this tells me that salaries are evenly distributed for the most part, with some really big entries towards the high end. Still, the $4,000 shift isn’t too big, right?

Wait, this actually is an issue…

Remember when I said 2 seconds ago that $4,000 was a significant but not crazy large? Well, unfortunately the skew in the data set really screws up some analysis we want to do. Specifically, our friend Standard Deviation. We need a reasonable standard deviation to do a standard error analysis, which we cover later.

Standard Deviation is a measure of the spread of a distribution. Are the numbers clumped near the mean, or are they spread far out? The larger the standard deviation, the more variation of entries.

If a distribution is a roughly normal distribution, we can predict how many results will fall within a certain range: 68% within +/- 1 standard deviation, 95% within +/- 2 deviations, 99.7% within 3 deviations.

Well, because of the way standard deviation is calculated, it is especially sensitive to outliers. In this case, it’s extremely sensitive. The standard deviation of all the raw data is $66,500 . When I remove results outside of $15-$165K, the standard deviation plummets to $32,000. This suggests that there is a lot of variability in the data being caused by 5% of the entries.

So let’s talk about how to remove outliers.

Removing Outliers

Identifying the IQR

Remember when I got that $15,000-$165000 range? That’s by using a tool called InterQuartile Range or IQR.

It sounds fancy, but it is incredibly simple. Interquartile range is basically the distance between the middle of the bottom half and the middle of the top half.

So in our case, if we take the bottom half of the data, the median salary is $65,000. If we take the top half of the data, the median salary is $115,000. The IQR is the difference between the two numbers, which is $50,000.

Using IQR to filter out outliers

Okay, so we have a spread $50,000 between the first and third quartiles. How do we use that information? Well there is a common rule of thumb that anything outside +/- 1.5 IQR is an outlier. In fact, when you see a boxplot, that is what is going on when you see those dots.

So, $50,000 *1.5 is $75,000. If we take the median ($90,000) and add/subtract 75,000 we get our earlier range of $15,000-$165,000

Standard Error Analysis

Okay, so why did we go through all that work to get the standard deviation to be a little more reasonable? Well, I want to do something called a Standard Error Analysis to answer the following question:

What if our sample is a poor sample?

What is our average female salary is lower because of a sampling error? Specifically, what are the odds that we samples a lower average salary by pure chance? “Poppycock!”, you might say. Well, standard error gives us an idea of how unlikely that is.

Importance of sample size

Let’s say there are only 1,000 female DBAs in the whole world, and we select 10 of them. What are the chances that the average salary of those 10 is representative of the original 1,000? It’s not great. We could easily pick 10 individuals from the bottom quartile, for example.

What if we sampled 100 instead of 1000? The chances get a lot better. We are far more likely to include individuals that are above average for the population as a whole. The larger the sample, the closer the sample mean will match the mean of the original population.

The larger the sample size, the smaller the standard error.

Importance of spread

Remember before we said that a reasonable standard deviation is important?  Let’s talk about why. Let’s say there are 10 people in that bar, and that the spread of salaries is small. Everyone there make roughly the same amount. As a result the standard deviation, a measure of spread, is going to be quite small.

So, let’s say you take three people at random out of that bar, bribe them with a free drink, and take the average of their salaries. If you do that multiple times, in general that number is going to be close to the true average of the whole population (the bar).

Now, Bill Gates walks in again and we repeat the exercise three times. Because he is such an outlier, the standard deviation is much larger. This throws everything out of whack. We get three samples: $50,000, $60,000, and $30,000,000,000. Whoops.

The smaller the standard deviation of a population, the smaller the standard error.

Calculating Standard Error

Getting the prerequisites

To calculate the standard error, we need mean, standard deviation and sample size. Before we calculate those numbers, I want to narrow the focus a bit.

I’ve taken the source data, and narrowed it down to US, DBA and within $15,000-$16,5000. This should give us more of an apples to apples comparison. So what do we get?

We’ve got a gap in average salary of about $4,500.  This seems quite significant, but soon we’ll prove how significant.

We’ve also a standard deviation of around $24,500. If salaries full under a normal distribution, this means that 95% of DBA salaries in the US should be within $52,500-$151,000. That sounds about right to me.

Calculating individual standard error

So now we have everything we need to calculate standard error for the female and male samples individually. The formula for standard error is standard deviation divided by the square root of the count.

So for females, it’s 23,493 / sqrt(123) = 2118. This means that if we were only sampling female DBAs, we would expect the average salary to be within +/- $2,118  about two thirds of the time.

So, if we were to randomly sample female DBA’s, then 95% of the time, that sample’s average would be less than the male average from before.

That seems like a strong indicator that the lower average salary for females isn’t just chance. But we actually have a stronger way to do this comparison.

Standard error of sample means

Whenever you want to compare the means from two different samples, you use a slightly different formula which combines everything together.

The formula is SQRT( (S_a^2 + S_b^2) / (n_a +n_b)) . S is the standard deviation for samples a and b. Standard deviation squared is also known as the variance. N is the count for samples a and b.

If we combine it all together we get this:

The standard error when we combine samples is $1,154. This indicates that if there was no difference between the distribution in female and male salaries, then 68% of the time they would be within $1,154.

Well in this case, the difference in means is almost 4 times that. So if the difference in means is 3.88 standard deviations apart, how often would that happen by pure chance? Well, we would see this level of separation 0.01% of the time, or about 1 in 10,000.

Conclusion

I take this as strong evidence that there is a real wage gap between female and male DBAs in the USA.

What this does not tells us is why. There are a number of reasons that people speculate as to the cause of this gap, many in Brent’s original blog post and the comments below it. I’ll leave that to them to speculate what the cause is.

A couple of people were asking for the code to my demos, so here it is! I’m utterly exhausted, but it was such an honor presenting to so many people. My favorite part was getting to thank a hard-of-hearing gentleman in sign language for asking a question at the very end.

Eugene Meidinger – Execution Plans 101_11-01-2017