Data analyst here. It really do be like that. You can use stats to prove anything.
Yfw they say data doesn’t lie. Looool
Data analyst here. It really do be like that. You can use stats to prove anything.
Yfw they say data doesn’t lie. Looool
Well, back in 19 and dickaty-2 there was whisperings of a movie tie-in. The money spent, well I won’t bore you with that, wouldn’t mean much these days. But let’s say it was epic - do people still say that? Boy, they used to.
Anyway, they rushed to production and built a million billion cartridges. Do you know what those are? These little black boxes that had the whole video game on these massive chips. Of course they were small in those days.
So they send it to market and it doesn’t work. So then nobody bought it. And did you know, they buried all those cartridges way out in the desert some where, and that’s where the aurora borealis comes from - the sky used all those chips to paint pretty pictures. And the video game industry began a bloodless vendetta that’s still around today, to make up for that blunder by making as much money as possible, even if the game’s not worth it.
Come gather round the stump young’ns, and I’ll tell you a tale of when video games didn’t need to be connected to the Internet.
Do you have any videos? Can you record tracks and musical production type stuff?
Funny story, but she knew, and wanted to see what he would do.
If you’re really interested…
Let’s say you want to know how an ad has affected your sales since it was released 3 months ago.
You could put every single sale as a dot on a graph, but it probably wouldn’t mean anything. Even if it showed the dots gradually getting higher on the chart. Was that caused by the ad or does it happen every year at the se time? What other factors could have caused this.
So I’ll pause right there and say you will never know. You will never know all the forces that affect trends. You can get relatively close, but not. Does weather affect your sales? Delivery time? Internet sentiment?
So that’s not very scientific, right? You need to know and control all variables to test an outcome.
Anyway, so you have a graph with dots and it may or may not mean anything. You think, ok what was last year’s sales during these same 3 months?
So you get last year’s data and plot the sales as dots in a different color. Now you have a graph with a ton of dots of two colors, and best case scenario: the dots for this year are higher than last year.
Is it responsible to stop there? If it were me, and my money, I’d want to make sure. So then you’d compare data from two years ago. Now you have a chart with three colors of dots.
Again, best case, this year is higher than that year too. However, as always is the case, the dots are getting difficult to understand, especially for people that don’t know anything about data. You need to make things simple to digest.
So you say “I’ll make an average of each month” and that will show how the averages are getting bigger, compared to previous years. Great!
So you average all the dots by month and plot them on a graph, and it looks great. But there are a few months that don’t prove what you saw in the raw data. For instance, one month, two years ago, you landed a big contract and sold an astronomical number of units. So that month is the biggest one of all.
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Ok, no problem, you’ll just remove those two data points, because they are skewing the day. Again, this is best case. Most of the time you will not be sure if these data points are errors in the data or Genuine sales. But anyway…
Luckily there is a method for removing “outliers” it’s called standard deviation, and it’s basically an equation that figures out what is an acceptable outlier and what isn’t.
Again, I’ll pause here to point out how unscientific this is. You are removing data because it doesn’t follow the trend you want to show. And this is a perfectly acceptable practice in data analytics. And I’ll point out something else, what was the affect of those contracts on your normal business sales? Did you make relatively less sales because of it? Is it responsible to completely remove those sales? Is it ethical?
And this is all very minor stuff in analytics. The more detailed the question, the more the data is “cleansed” by equations that get progressively more complicated - the more ethically vague the data is.