(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Ulrich Atz writes:
I regard myself fairly familiar with modern “big data” tools and models such as random forests, SVM etc. However, HyperCube is something I haven’t come across yet (met the marketing guy last week) and they advertise it as “disruptive”, “unique”, “best performing data analysis tool available”.
Have you seen it in action? Perhaps performing in any data science style competition?
On a side note, they claim it is “non-statistical” which I find absurd. A marketing ploy, but sounds like physics without math.
Hence, my question:
Do you think there is such a thing as a (1) non-statistical data analysis and (2) non-statistical data set?
Here’s what’s on the webpage:
The technology is non-statistical, meaning it does not take a sample and use algorithms in order to validate a hypothesis. Instead, it takes input from a large volume of data and outputs the results from the data alone. This means that all the available data is taken into account.
I’m not quite what’s the difference between “take a sample” and “take input from a large volume of data.” All their examples involve generalizing from their sample data to a population. This sounds statistical to me.
The webpage continues:
The lack of a hypothesis is another advantage of HyperCube over statistics. HyperCube exposes the rules and dependencies that are indicated by the data, and is not tied to any previously held view. Statistics, on the other hand, test data to see whether it proves a specified scenario.
Again, to me this misses the point. The available data are not the point, they are a means to the larger goal of making predictions about future cases.
That said, even if the authors of this press material are confused about statistical inference and sampling, the software package could be good. I have no idea.
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