Statistics for research sucks - I'm here to fix it

You like science.
Research, discoveries, the unknown.
The lab, the equipment, the techniques that seem like magic.
All of this fascinates you.

Awesome, we have the results!

A bunch of numbers.

What do I do now? Heteroscedascity? Shapiro-Wilk? Who's that?

It's not that cool anymore.

Unfortunately, we're told that science is a bunch of nerds searching for the unknown wearing white coats. But it goes way beyond that.

You need lots of knowledge, not only on the area you're specifically studying, but also in transversal areas, such as engineering, and math, more specifically, statistics. And this, this scares researchers. You entered research for the fun, not for the numbers. But, how can you confirm that what you are saying actually (somehow) represents the reality? Or, at least, part of it?

You need statistics. However, is not intuitive, is a completely different and new world, and you don't know where to start.

I was this person, and I had to learn the hard way. So hard, that I did a PhD in Biotechnology using Machine Learning to diagnose cardiovascular disease. Believe me, there's a lot of statistics in ML.

That's why I created Hepta AI, an AI-powered statistics tool for scientific research. You just paste your data, and the AI does everything for you. Runs all the boring (but necessary) tests, creates tables and graphs, the results and description for you.

Easy right? The way it should be.

We have a waitlist open, so, if you want to try it out, just go to usehepta.com and join the list.

We are also running a LTD for 97$, buy once and use FOREVER, with all updates and new features.

See you there!