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Generally speaking, IMHO, making the interface easier, in practice, doesn’t actually make using statistical methods correctly easier and that’s scary.SPSS is a classic example - and the social sciences have had a series of discredited papers over recent years due to poor application of statistical methods. Just because you can put a dataset in and get values out - even values that look significant, it doesn’t mean they are - did all of the assumptions and requirements of the methods/tests you used hold true/pass with your data?So, while I haven’t looked closely at this tool, all I saw was talk of the interface and the ease of getting results even if “you don’t know where to start”. That scares me. Especially when you start talking about applications in domains like medicine. That could be lives in the balance. Would we want civil engineers using tools like this to build bridges?

Sure it will work with a Mac. I've had the savings bond wizard on my Mac for almost two years. It's a web based program so it shouldn't be a problem. Use this link and just click on 'download wizard 4.15' and your dmg will pop up and start the download.

Or people designing nuclear reactors?To me, this is worse, not better unless it somehow helps you actually understand when, why and how to correctly use these tools. To be fair, R isn't really that accessible to most social scientists. Heck, R isn't really that accessible to most programmers either. The tidyverse and R notebooks have definitely improved things by leaps and bounds, but R at its core is an unusual language. (I say this as a somewhat proficient R user) Its programmatic nature does aid in coming up with reproducible data analyses though.SPSS has, shall we say, a less than savory reputation.All this to say that there is a market for something much friendlier than R.

R is used by pure statisticians, data scientists and the like, but most social scientists prefer Stata, which has pretty legit statistical routines as well as a point-and-click UI. As a proficient R user, though, you probably agree that it makes a certain sense when you get into a rhythm.I think it surprisingly makes a lot of sense to social scientists, in spite of seeming backwards to computer scientists. I remember being in school and using STATA, and just plugging in numbers to get through exercises too quickly to bother understanding what the labs were about.R seems to make you stop to realize what you are doing at the right moments, then uses a lot of magic to abstract away almost everything else. Heck, R isn't really that accessible to most programmers either.R is objectively a bad programming language. However, it is by no means inaccessible.

I have no statistics background whatsoever, and I managed to learn enough R to be dangerous in a mere week. Other than the 1-based indexing and the utterly disgusting dynamic dispatch mechanism (you could simply not to use the latter), R is surprisingly pleasant to use. What I enjoyed the most is that vectors and matrices are first-class values, not objects that are referred to through pointers. It's probably copy-on-write under the hood, but I don't need to care. This is a well known problem in cognitive systems engineering, which sometimes seem to promote design which might seem backwards to an interaction designer focused on creating for everyday consumption.You don't want to design the control room for a nuclear reactor like you design a webpage, because you can under no circumstances sacrifice correctness for ease. A lot of times, it's about making the wrong thing really hard to do, with multiple layers of fail-safes. The potential gain in productivity when designing for ease is vastly overshadowed by the risks associated with making an error.When we're not talking about nuclear reactors, but—for example—statistics, where consequences are abstract, it might get tempting to err on the side of ease.

In fact, any domain you don't understand well enough well tempt you to design for ease over correctness.Ideally, the designer should understand the domain at hand well enough to create a design that makes it easy to be correct, and hard to make an error.There's a lot of stuff out there that has been neither designed for ease nor correctness, however, but which has a design arbitrarily dictated by the table layout of a database, or some other random technical constrain that has nothing to do with the problem domain. I use Wizard all the time. I'm not a data scientist, but I often need to make engineering decisions based on trends hidden in large datasets. Wizard makes it easy to find what I'm looking for. I'm generally using it for a first pass at data, and when I find the trend, outlier, correlation, etc, that I'm looking for, I'll then move on to a tool with more features like Tableau or JMP to really dig in.What is wrong with having a fast and easy to use tool which makes data analysis accessible? If people misuse their data or their tools, that's their fault. Excuse a brief bit of hyperbole, but what’s your stance on gun control?

Like it or not, SPSS made science much easier.No - it didn’t. Science isn’t running stats tools. It’s knowing what tools to use and using them properly so that you get valid answers.If you haven’t been paying attention, the social sciences have been plagued by an epidemic of failures of reproducibility - see: for reference. Only 39% of studies were found to be replicable and much of the blame falls to poor methods and techniques like p-hacking. And we should work in that direction.Yes, we should - but dumbing down interfaces in ways that don’t actually provide good guardrails/handholding to help with proper application is not making good science easier. Not creating more complex tools for no reason.Nowhere have I or others suggested that.

But a tool that makes it simple to get an answer is not the same as one that makes it simple to get a correct and valid answer.Science is a means to an end in many cases. Medicine, for example, is about better human lives. Shoddy research methods could do anything from giving false hope to actually endangering lives.

I use Wizard all the time at work for analyzing manufacturing data to quickly check for trends and correlations. I find it better, easier, and faster than the internal tools purpose-built for the same task. I also prefer Wizard over JMP, Tableau, or any R/numpy/gnuplot methods, specifically for one-off tasks and analyzing new issues.I’m not going to write a script or configure complex software for a quick check.

Wizard is perfect for that. It’s also super fast at scanning through really enormous CSV files, then generating plots for every parameter.Every coworker who sees me using it for these tasks wants to know what it is, or how I send out relevant plots so quickly when new datasets are available. It’s really a fantastic tool for quick work.There seems to be a lot of negativity in this comments section about misuse of statistics. I think people are missing the point. Easy tools make data analysis more accessible, but misuse of data is the fault of the user, not the tool.

Robust statistical analyses require knowledge, judgement, and increasingly, specialist expertise. To market a product as a way to jettison the statistician is so shortsighted as to be intellectual malpractice.Forgive me if I seem overly aggressive but I have grown weary of my and my colleagues' profession being side-lined and belittled. Politicians, administrators, and even some other scientists see statistics as merely a badge to be placed atop their own work for validation.

Well, ladies and gentlemen, statistics is more than that. It is an empirical science in its own right.Of course, it doesn't always take a statistician to do the necessary statistical work. I am no physicist yet I can certainly apply the Clausius Clapeyron equation as needed.

Likewise, I expect many (perhaps most) scientists to be able to apply an ANOVA or simple regression as the need arises.HOWEVER, the lack of intellectual humility on the part of so many non-statisticians when applying statistical tools to their own work is maddening. I don't understand the hostility to this application. Progress comes from trying different ways to do things. I tried this program a few years back, and thought it was ok. It is easier to use than JMP, which some others have mentioned, though not as powerful.There is nothing about easy-to-use that precludes understanding, and certainly nothing about difficult-to-use that promotes it.

They are largely orthogonal. How to rotate pictures on polaroid digital picture frame. Using R doesn't make you a statistician, any more than using C makes you a software engineer.

If anything, a simple interface can reduce the number of ways you can shoot yourself, and leaves more time to focus on the problem.Being easy-to-use may be the difference between some analysis and no analysis, or at best, analysis by spreadsheet.And finally, this is Hacker News. The author wrote this software and makes some money off it. Isn't that what this place is all about? I don't understand this program. It states it is a statistics program but on the front page only test stated is ' Shapiro-Wilk'. I don't know how many here are familiar with statistics, but that is basically the hello-world thing for statistic tests. Also the pricing puts this program directly at the range of Graphpad's Prism, which is widely-used in academic fields other than the field of statistics, and quite intuitive.Making good-looking figures nowadays is not a selling point anymore.

If one's willing to script rather than clicking-the-mouse, Prism, Igor Pro, Origin Pro, Matlab (pricing from low to high) all can produce great figures and solid statistical test for people out of the statistics field. But nothing these days beats R for versatile of statistical tests. Apart from being literally the opposite of where I think statistics should be headed (i.e. I see the notion of 'removal of 'complicated statistics knowledge' to be more dangerous than helpful), I also had some practical feedback from watching the 12 minute intro video.-Firstly, how does the visualization know the respective population or sample size from which the summary statistics and intervals are to be drawn?- The demo used a pie chart to try to display summary stats and confidence intervals from the general social survey.

Aside from professional statisticians general dislike of pie charts, you cannot plot confidence intervals in this way into a pie chart just by inserting 'more white space between the slices'. There's only 100% of the area of the circle that you've got to play with, so any attempt to increase the 'white space' between the slices necessarily warps the real estate remaining to represent each actual slice.- Honestly, I see this tool likely to be used by people who participate in the practice of p-hacking, whether deliberately or not. The ability to throw lots of simple models quickly at lots of data mindlessly reporting some notion of statistical significance is dangerous. I'm assuming your stats are not (cannot) be adjusted in any fashion to implicate what you're really doing by using an automated model-building/reporting regime in this way (potentially running heaps of models on heaps of data until you find one that appears 'significant' based on a statistical test designed under the assumption that this is NOT what you're doing).

No where did i see any application of train/test, sample/resample type methods to try to control for over-fitting in the prediction application or truly estimate how predictive/replicable such a technique would be in the real world.While I appreciate the work done required to put something like this together (a lot of it looks like a gui interface to my own exploratory functions/scripts in R, for example), i genuinely believe this approach is more dangerous/likely to lead to false conclusions than helpful. I agree, in this case, you’re right on (and I have another comment saying as much) but couldn’t there be a UI that guides you safely through some of those dangers? I have yet to see anything close to one but I could imagine a system that would at least ask a few questions that would quickly disqualify a dataset or intended analysis for some use cases.

Imagine, if you will, turbo-tax for (basic) statistical analyses.As I said elsewhere, a simple interface doesn’t necessarily mean a correct outcome -In short, most stats software is solving the wrong problem. Many (especially this one) make it easy to get an answer, right or wrong. I’d rather see them make it hard to get a wrong answer - or, perhaps, hard to get an answer when you’re using it wrong. I understand that by answering in the negative (no, its not possible), i would put myself into the '64k is enough for anybody' type comments. Which is to say, since its not a logical impossibility and not a well defined concept and our technology/understanding is increasing, odds are we'll at least make headways towards it to the point that its already pretty good.

One could argue that we're already there compared to statistical environments of the 80's and 90's.Honestly, I think this is a tricky human problem, not a tech problem.My reasoning is thus. SPSS user here, I run a bunch of surveys in Qualtrics and SurveyMonkey, and frequently use their export to SPSS file functionality.Often, I have questions that take the format of 'for each of the following categories please rank them between strongly disagree and Strongly agree'. The way these questions end up in the SPSS files are typically as different variables for each row, and a 1,2,3,4, or 5 as the measurement along with the labels.Frequently, I want to pivot those types of questions by a variable like Region or Number of Employees (categorical), and then see the resultant tables. This is never fun, and inevitably takes a lot of time.As others have said, statistics is a careful business that doesn't necessarily warrant ease of access to all mathematical functions BUT, handling what SPSS calls 'Multiple Response Sets' better would be a godsend, just for the data prep and visualization step. I still ultimately fall back on recoding these or leveraging the MRS functional in SPSS to get this done (sometimes this is better than just using pivot tables in excel).It would be great to be able to specify this kind of thing in this program, since without it, you can't really use/trust the computed percentages in some question configurations. Take a real look at the SPSS Tables feature, the Multiple Response Sets, and then visualization of them, and consider how that data is actually coded in SPSS files (the common export of survey tools) and maybe you can improve on that feature (It shouldn't be hard, MRS is a pretty bad setup, but it gets the job done).

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