It's unclear what you t-tested here. Ideally, you would test for difference between groups of "Is there a difference in number of endorsements between people who got a "yes" in advancing to the next round or not". As a followup, is there a difference between those who's preferred was most endorsed or not?
I'm a bit stunned that you didn't recognize Language as programming language...... :(
As an example, people probably get endorsed for SQL or CSS far more than their programming language of choice that is tested in an interview.
If you breakup the plot by any number of categories of technical ability, then there are trends. But to your question, what I was suggesting was that the aggregations done, especially with categorical data that is averaged, are very susceptible to this. And those clusters are reminiscent of situations like this:
I'd more so like to see this analyzed against who got to the next round (their binary signal), or yes against preferred language, which I suspect will be much more telling.
The takeaway from that plot is, there is more to the story.
I really appreciate your sharing the data with us and I like your service. But, this is a poorly done and a far from subtle plug of your business at the cost of LinkedIn.
1) Technical ability vs # of endorsements
Jesus. Hiding stats that you don't like through aggregations? And please read up on Simspons Paradox, which is clearly the case here just by looking at your plot. Try a basic t-test, or rather some statistical rigor, the next time you try to make conclusions from data.
2) Most endorsed vs Language of Choice
As pointed out, this is not the way to frame your problem. By obfuscating what's happening in your histogram (which isn't technically constructued right either) you are again hiding what you dont like through aggregation. By the way, language matters greatly here, and you'd have benefitted by standardization.
3) Your conclusion
"After running some significance testing, though" and not posting your results or methodology, which is at best questionable after reading your analysis.
Again, I enjoy your service, but blog posts on technical ability that are ironically lacking in technical ability don't really make me want to come back.
PS: A little birdie told me that endorsements are quite strong in predictive power for jobs :)
World class engineers (especially data eng) who built some amazing open source things, but internally was a mess.
The reason LinkedIn is a terrible product is because it was mired internally by chronically poor engineering leadership and product politics. Nothing ever got shipped. This new rollout is nice but 5 years too late.
I'm really hoping the MSFT buy will change things, as I really believe in the product and there are some great engineers there.
Honestly, the paper wasn't bad, but I'm shocked they let this article get out.
Combine the author's incredibly poor understanding,shit link bait headline, and baseless article with ZERO results or novelty and this is 10 minutes of my life I want back.
No, you just had a bad point that you'll come to realize if you ever have to manage a team or company.
More often than not, you outsource items that are not the highest value projects your resources could be spent on. Or, you simply cannot afford to hire a specialist for a specific need. Try to find me experts for some of the pattern recognition algorithms offered who will work at your shitty startup, your shitty big-co with minimal tech wing, or your random mid-sized in the middle of nowhere. I just described 95% of companies on the planet.
And to second relix's point, try to justify implementing something that may have taken years to optimize, even if you think you can do it better. Also, do you want to maintain it? What happens if/when the company loses you? Would they rather have some poorly documented, poorly optimized in house version with nobody around who knows how it works or have a contract with another party they can count on for maintenance and support?
Is this always the case? No. Sometimes it's a core function you want to develop in house. But, it's usually considered.
As a developer, a big step in growth is realizing how to best spend your time and resources, and realizing that contracting implementation of X to further speed up building Y is a choice you'll often have to make.
Every model's output quality is dependent on the quantity of data it ingests.
Statistics developed as a science because of the need to overcome the weakness of large samples being expensive. Machine learning has taken off as a direct result of the field's ability to take advantage of and get serious performance gains from the massive amounts of data being generated and leveraged recently.
Here is the best summation I can reference, and I can tell you from personal experience it is very true:
"The accuracy & nature of answers you get on large data sets can be completely different from what you see on small samples. Big data provides a competitive advantage. For the web data sets you describe, it turns out that having 10x the amount of data allows you to automatically discover patterns that would be impossible with smaller samples (think Signal to Noise). The deeper into demographic slices you want to dive, the more data you will need to get the same accuracy."
They do [1], but not as often as you expect. Most games don't actually use AI, as the goal is to make in game characters appear intelligent, rather than actually being so.
What I've learned in the months since is that in order to overcome the panic and anxiety you have to do something very simple yet seemingly incredibly difficult; you have to let it happen. Let the panic wash over you
I must not fear.
Fear is the mind-killer.
Fear is the little-death that brings total obliteration.
I will face my fear.
I will permit it to pass over me and through me.
And when it has gone past I will turn the inner eye to see its path.
In the same aggressive tone you're holding towards the article, you probably should have read the publication before criticizing the experiment.
Quick lesson on pharmaceutical studies: they are extremely expensive and often can only have a limited sample size.
Quick lesson on statistics: it was developed to deal with small sample sizes and allow you to generalize to larger populations.
As such, pharmacy has very strict statistical standards with how to setup, design, and analyze experimental results. I'm a statistician, and I was impressed by how well designed this experiment was.
More so, the study only aimed to find a more precise cause of the issues. And, even with the solid analysis they had, are simply saying, "Hey, we should question this anti-gluten thing, it might not be as big an issue as we think".
I believe you are repeating what I said (?). All of the algorithms have different methods of arriving to an objective function and leveraging it's results. Yet, most share the same problem in terms of optimizing it, and yes, most choose other routes.
It's unclear what you t-tested here. Ideally, you would test for difference between groups of "Is there a difference in number of endorsements between people who got a "yes" in advancing to the next round or not". As a followup, is there a difference between those who's preferred was most endorsed or not?
I'm a bit stunned that you didn't recognize Language as programming language...... :(
As an example, people probably get endorsed for SQL or CSS far more than their programming language of choice that is tested in an interview.