Simply Statistics

“Simply Statistics” is a comprehensive resource that delves into various aspects of statistics, providing insights and knowledge on data analysis, interpretation, and application. The content covers a wide range of topics, including machine learning, data augmentation, Python programming, and AI applications. With a focus on practicality and real-world relevance, the source offers valuable information for both beginners and experienced professionals in the field of statistics. By exploring the referenced documents, readers can gain a deeper understanding of statistical concepts, data manipulation techniques, and the integration of advanced technologies like AI and machine learning in statistical analysis.

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Universities Do Spend Indirect Costs on Research, And It’s Still Not Enough

Simply Statistics

After the recent announcement from NIH about reductions in NIH funding: Last year, $9B of the $35B that the National Institutes of Health (NIH) granted for research was used for administrative overhea...

Biologists, stop putting UMAP plots in your papers

Simply Statistics

The UMAP craze in singe cell RNA-Seq Single-cell RNA sequencing (scRNA-seq) has become one of the most widely used technologies in basic biology. With the rise of scRNA-seq, the use of UMAP has becom...

Russell Shinohara is awarded the 2023 Mortimer Spiegelman Award

Simply Statistics

Russell Shinohara is awarded 2023 Mortimer Spiegelman Award Russell “Taki” Shinohara was selected as 2023 Moritmer Spiegelman Award recipient. Dr. Shinohara was selected from an incredibly deep and ta...

The analyst is a random variable

Simply Statistics

I read this really interesting paper over the break, where they had multiple analyst teams analyze the same data set and fit a model to answer the same question. This is a topic we’ve thought about a ...

What is artificial intelligence? A three part definition

Simply Statistics

Editor’s note: This is the first chapter of a book I’m working on called Demystifying Artificial Intelligence . The goal of the book is to demystify what modern AI is and does for a general audience. ...

Is Code the Best Way to Represent a Data Analysis?

Simply Statistics

I’ve spent the better part of my career advocating for the increased publication of code that is used in data analysis. This effort to make data analysis more reproducible is largely focused around pr...

Narrative Failure in Data Analysis

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In data analysis there is often a distinction between doing the data analysis and communicating the data analysis. The idea is to analyze the data and then come up with some sort of narrative that exp...

Thinking About Failure in Data Analysis

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What does it mean for a data analysis to fail? I’ve come to feel that this is an important question because considering the ways that data analyses can fail is a good way to prevent an analysis from a...

Streamline - tidy data as a service

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Tldr: We started a company called Streamline Data Science https://streamlinedatascience.io/ that offers tidy data as a service. We are looking for customers, partnerships and employees as we scale up ...

The Four Jobs of the Data Scientist

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In 2019 I wrote a post about The Tentpoles of Data Science that tried to distill the key skills of the data scientist. In the post I wrote: When I ask myself the question “What is data science?” I ten...

Palantir Shows Its Cards

Simply Statistics

File this under long-term followup, but just about four years ago I wrote about Palantir , the previously secretive but now soon to be public data science company, and how its valuation was a commenta...

Asymptotics of Reproducibility

Simply Statistics

Every once in a while, I see a tweet or post that asks whether one should use tool X or software Y in order to “make their data analysis reproducible”. I think this is a reasonable question because, i...