Show HN: Statwing is statistical analysis, simplified

Thanks for chiming in.

Unfortunately, we don't have regressions yet. But just to make sure these findings were still valid, we tossed this data into a program that did to make sure the effect remained (it did).

It's no substitute for regressions/ANOVA, but for now here's what Statwing can do: If you add a filter that excludes datapoints below, say, 40 years old. Looking only at folks older than 40, there's no relationship between [neuroticism and age], but the relationship between [neuroticism and preference] remains.

But, point taken. We'll count this as a vote for prioritizing regression. Thanks!

For example, let's say people measure neuroticism exponentially, and an increase of one point means 10x perceived neuroticism. Because mean(log(x)) != log(mean(x)), the average won't be representative.

Everything else looks OK though: count, median, percentiles, a histogram.

Agreed, Likerts are definitely an area of controversy.

A couple references from Wikipedia about Likert-as-continuous:

Pro: http://www.ncbi.nlm.nih.gov/pubmed/20146096

Con: http://xa.yimg.com/kq/groups/18751725/128169439/name/1Likert...

Our stance is generally a pragmatic one. It's very common practice to analyze data this way, so we enable it. But if you want to analyze your Likerts as ordinal data, you can change the variable type to Ranks (ie Ordinal), where we handle the result as you'd expect (nonparametric test, no averages). Note that this feature is disabled in the demo.

Thanks!

edit: formatting

Now that I look at it more, the front page buttons do look weird compared to all of our other buttons. We've become numb to it after looking at it so often. Most of our buttons do the design thing that you described, so we'll change that shortly! Thanks for the feedback.

Thanks for the question. Cheers!

Circle 1. A few specific analysts in a few specific companies we're associated with. They analyze survey data, they use only basic functionality of the fancy tools, and they want a simpler solution.

Circle 2. People analyzing surveys generally. It's a straightforward application where existing tools are way too complicated.

Circle 3. The rest of the 50% of stats tool users that never use more than the core functionality of existing tools (that number is from our research).

Circle 4. People who analyze at work. In particular, Excel power-user analysts and marketing folks for whom the go-to tool for analysis is the pivot table. We want to ease them into the world of more powerful, statistical analysis. We do a lot of usability testing with these folks and we're excited about their reactions so far. But they're not in a lot of pain, so they're not a great initial audience for us.

Grand vision stuff: Tools like SPSS and the like were built in the 80s, and Excel pivot tables were built in the 90s. They've been updated but not overhauled, and there's a gaping hole between them in terms of ease of use and power. As small, rich datasets become ubiquitous, are people in 2020 really going to be using tools from 1990? We hope not.

Ultimately we'll need to address this. Hopefully doing so (automatically) will differentiate Statwing from other stats package, where one is quite free to shoot one's self in the foot (and one often does).

We'll count this as a vote for the prioritization of that feature.

Thanks for the comment, really appreciate it.

But there's a whole class of overlooked folks who need to do statistical analysis on smaller datasets on more of a weekly or several-consecutive-intense-days-per-month basis. These folks, who split time between Excel and stats tools, make up a surprisingly high proportion of the user base of stats products. And they tell us they're willing to pay for something that makes their analysis and communication more efficient.

Thanks for the comments, and for the kind words RE the idea and execution.

edit: And to be fair to your point, we're sort of comparing apples and oranges insomuch as you're looking at what we have now (not nearly enough) and we're looking at our roadmap for what we'll have in six months, a year, etc.

What if this could improve statistical literacy?

Streaming algorithms do exist for many basic statistical measures. But in many other cases, the best streaming algos aren't cheap or accurate enough to be useful.

Bucketing can sometimes substitute for a bona fide streaming algorithm. But again, there's plenty of cases where bucketing won't work well enough to make it useful.

I haven't really looked at Statwing yet--the premise is really tantalizing, though. Gotta find an excuse to throw a spreadsheet in there and see what comes back.

Just for reference, everything's at 95% confidence. We do mention that in the Advanced output but it's perhaps a bit too hidden.

We are trying to make Statwing automatically display the right analyses for the portions of your data you are most interested in.

If we can accomplish that, then hopefully we've helped make you faster at understanding the relationships in your data. Maybe that is enough so you don't need to break out R for basic analyses. Otherwise I would also use R. I made some graphs in R that wouldn't be able to do in Statwing right now, but if we can output the right things based on your data then hopefully you could save some time with us.

Agreed that quite often the hardest part is getting the data together (particularly on the web). But from our perspective, it's still true that conducting the actual analysis and visualizing the data should be a lot easier than it is. And that's particularly true if you're in our initial audience, the roughly 50% of SPSS/R/Minitab/etc. users who never use anything past the basic functionality of those programs.

I guess a simpler answer is that we think there's a need because this is a product that I badly wanted when I was an analyst/consultant, splitting time between Excel and the basic functionality of SPSS.

edit: Also, and this isn't very helpful, but we talk to a ton of people about their data analysis needs, and we hear a good chunk of them talk a lot about the pain of using highly technical solutions for relatively simple problems like analyzing a survey.

I don't know who the creators are targeting, but perhaps it's not experienced people? One of the great parts of the internet is the way it seems to lower the barrier of entry for just about everything. If this app can help people learn how to do simple statistical analysis, there just might be a load of value there. I didn't really click around, but something they might think about doing is allowing users to hyperlink directly to an "analysis session" so a blogger can not only link to a graph, but the data itself. I can imagine a scenario where a blogger writes a post about maybe housing prices, and draws some unreasonable conclusion. Then a reader goes, and adds in inflation data which changes the story. He then replies in the comments with a link to his new "analysis session" spurring a new conversation.

Its probably taking the tool to a different direction, but i really like what's here.