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Comment by edimaudo | Hacker News Reader

How can you run a good analysis without math?

Math is just one of the ways to think about problem. But you need not have a math degree to analyze data. People do it all the time even without math.

Data analysis needs understanding casualities and correlations, and ability to use some handy tools, like chi-squared. The more tools you can use, the better analyst you are.

I know some psycology scientists who have no math background at all, but who can setup an experiment and do statistical analisys on data gathered. They have a pretty good understanding on what they do while its not strictly math understanding. Math is a tool and as with any other tool you need not to understand how a tool works, you need to know how to use that tool.

In recent times you even need not know nothing about calculations behind different statistical tests, because there are computers and software that are happy to calculate anything for you.

Data analysis without knowing math sounds like a great way to make a bunch of analysis mistakes. I know math decently and data analysis is still hard. The whole field of causality is becoming more rigorous - see the work by Judea Pearl, for example.

Yes, of course, math is needed, when you try to do something more complex than just separate two hypothesis with a ready-made method. But to start with data analisis you need no math. It is about different levels of qualifications.

Isn't this the type of thinking that led to the replication crisis?

The repication crisis is rooted not in math but in between math and reality. Math do not work with reality, math lives in its own ideal universe. To use math for data analisis we need reason about how much data we need to separate hypothesis, we need choose acceptable p-value, we need to choose math methods. There are guides how to choose method for data, that guides needs no math but understanding what experiment is. There are 'experimental psychology', and a bunch of good books on this topic, covering all concepts that one needs to speak about choosing math methods. As for p-value and sample sizes -- there are agreement that experiment needs ~30 people and p<0.05.

If we add something like ability to use R or SPSS, then we get scientist skilled enough to setup good experiment.

The replication crisis is not error of individuals, it is system error. Psychology is much more complex than, for example, quantum physics, there are much more causal links in psychology and no one know even how to speak about mind, for example: is it possible to differentiate perception from memory or from thinking? Perception cannot work without memory, and there are no way to separate them as phenomena. Psychology is much more complex than psysics, and at the same time for physicist is is normal to have p<0.001 or sample size of 10k data points, while psychology is bound to p<0.05 (it is probability to get false positive) and sample size of 30. This is itself explains while physics have no replication crisis while psychology have one.

In order to judge whether your math matches reality, you need to understand the math in the first place though. If you don't, then you're bound to repeatedly make mistakes.

By doing good statistics. No-one cares whether a model's application is well founded. What matters is if it's predictions hold up statistically. This means lots of validation runs and defense against overfitting. That can all be done while viewing the model as a black-box.

Model knowledge certainly helps, but it isn't neccesary.

statistics ⊂ math

"Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data." /wikipedia/

The relationship between math and statistics is an interesting one. In my experience, "Statistics is a form of Math" is true when the math folks want it to be, and is a totally separate thing that doesn't belong in the club at all when they don't.

I've never seen a mathematician claim that statistics is not a branch of mathematics. Nor would that really make sense.

You can certainly practice statistics in a way that mathematicians would take issue with, but it's intrinsically a branch of math.

I've seen a number of mathematicians take umbrage at the idea of describing statisticians as mathematicians, or even applied mathematicians.

Thinking that we are somehow special makes us feel better. The truth is that we are not.

"Doing good statistics" implies doing mathematics. In my opinion the meme that statistics is not math is perpetuated by people who have experience with statistics and not much exposure to other areas of mathematics. It might not "look" or "feel" like a lot of other undergraduate math but that doesn't mean it isn't.

Just because you're not attacking problems formalized with the underlying probability theory doesn't mean you're not practicing mathematics when you practice statistics. It's not a separate discipline.

Sure, but when people say 'I don't like math' that is mostly about linear algebra, calculus and analysis.

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