One of my goals for this newsletter is to help turn the jumble of my scattered musings into coherent learning journeys for you, arranged by theme and searchable by keyword. How do you use them? Simply skim the headings and dive into whatever is interesting. (Completionist? Go top to bottom.)

Since I’m a recovering statistician, here comes my attempt to do it for statistics!

Which brings me to…

**Don’t waste your time on statistics!**

Hah, couldn’t resist. No seriously, though, it’s best not to assume you need statistics for everything. (That’s a classic lie told to sell stats classes, textbooks, and other paraphernalia.) * If you’re not sure whether statistics is for you or what the point of it is*, explore this section.

**Formats:**

Blog - bit.ly/quaesita_pointofstats

Podcast - bit.ly/dipod_pointofstats

Video - bit.ly/quaesita_stc010

## What even is (are?) data?!

* If you’re new to data* (or someone who pronounces it as though it has a capital D), it’s probably a good idea to cover this topic before wading into the rest of the swamp…

Included topics: **information, data, software, data summaries, plots, histograms, distributions, and a few other tidbits.**

**Formats:**

Blog - bit.ly/quaesita_hist

Podcast - bit.ly/dipod_hist

**Statistics for people in a hurry**

Ever wished someone would just give you * the big picture of statistics* in plain English? Let me try to grant that wish for you! I’ll zoom through all the main ideas in statistics in 8 minutes! Or just 1 minute, if you stick to the large font bits. The links inside the blog take curious readers to my explanations of the highlighted topics.

**Formats:**

Blog - bit.ly/quaesita_statistics

Podcast - bit.ly/dipod_statistics

**Statistics for people time on their hands**

Want to take a whole video course on statistics? Curious to find out what it was like to be Google’s most popular course of all time, Statistical Thinking? Not all 10 hours of footage have been uploaded, so bookmark the playlist if you’re hungry for the whole enchilada.

Video - bit.ly/statthinking

**Um… this is awkward, but what *is* a statistic?**

The way we use the terms these days, it turns out that analytics is the discipline that’s about calculating statistics, but statistics is the discipline that’s all about going beyond those statistics. (Speaking of awkward.) But what *is* a statistic? To find out, leap into my * intro to statistics vocabulary*, including

*,*

**sample***,*

**population***,*

**observation***,*

**statistic***,*

**parameter***, and*

**estimate***.*

**hypothesis****Formats:**

Blog - bit.ly/quaesita_vocab

Podcast - bit.ly/dipod_vocab

Video - bit.ly/quaesita_stc002 through bit.ly/quaesita_stc008

## Wait, what’s the difference between analytics and statistics?

I’m glad you asked! Understanding this will save you a lot of stress if you work with data. Learn more about the * professions of analytics and statistics* here.

**Formats:**

Blog - bit.ly/quaesita_versus

Video - bit.ly/quaesita_ytversus

### Bonus: Can analysts and statisticians get along?

Incompatible species in the terrarium? Blog - bit.ly/quaesita_battle

## How does statistics fit into data science?

It’s a subset! To learn more about the * data science umbrella *or look at

*? Explore here.*

**a short history of its subdisciplines****Formats:**

Blog - bit.ly/quaesita_datasci and bit.ly/quaesita_history

Podcast - bit.ly/dipod_history

## Bayesian vs Frequentist statistics

Ah, you have questions about * the two major philosophies of statistics*? I have a couple of

**videos**you might like.

Curious to find out whether you think like a Bayesian or a Frequentist? bit.ly/quaesita_ytbayescoin

What keywords tip you off about whether you’re in Bayesian or Frequentist territory? bit.ly/quaesita_stc011

## The basics of hypothesis testing

If you’d like to understand the logic of hypothesis testing taught in the typical STAT101 class, start here. Learn about * default actions, null and alternative hypotheses*, and the one sentence that summarizes everything.

**Formats:**

Blog - (1) bit.ly/quaesita_damnedlies, (2) bit.ly/quaesita_fisher, (3) bit.ly/quaesita_yesbutton

Video - (1-2) bit.ly/quaesita_stc012 , (3) bit.ly/quaesita_stc013

## Explaining p-values with puppies

* Statistical significance and the dreaded p-value* are notorious for being taught terribly to maximize confusion. Let me try undo that damage with puppies…

**Formats:**

Blog - bit.ly/quaesita_pesky answers your various p-value questions and includes a link to my puppies intro (bit.ly/quaesita_puppies)

Video - bit.ly/quaesita_p1 (start here and keep watching p2, p3, p4 if you want more)

### Bonus: Thoughts on the p-value controversy

Blog - bit.ly/quaesita_needles

## Probability basics

That said, the first few weeks of stats class often cover something else entirely. Keen to talk about **probability, combinatorics, and the birthday paradox?**

**Formats:**

Blog - bit.ly/quaesita_bday

Podcast - bit.ly/dipod_bday

## Experiments, correlation, and A/B testing

If you’re interested in using statistics to answer questions about * cause and effect*, this trio of blog posts is for you! Here’s the suggested order for attacking them in:

Do you use the word “experiment” incorrectly? bit.ly/quaesita_experiment

What is correlation? bit.ly/quaesita_correlation

How do A/B tests work? bit.ly/quaesita_ab

## Can we talk about bias now?

You’re right, I should probably have gotten to that sooner. Here comes a coronavirus-sponsored * lesson on statistical bias*…

**Formats:**

Blog - bit.ly/quaesita_bias

Podcast - bit.ly/dipod_bias

## Psychological perspective: Attempting statistics is hopeless if…

If you’re not aware of your * cognitive biases*, you’ll undermine your ability to make good use of statistics. Here are some tips on how to fight your brain’s mischief.

**Choose your format:**

Blog - (1) bit.ly/quaesita_inspired and (2) bit.ly/quaesita_confirmation

Podcast - (2 only) bit.ly/dipod_confirmation

## Is statistics subjective or objective?

Subjective. (Sorry!) Want to learn more about the relationship between * capital-T-Truth, statistics, and science*?

**Formats:**

Blog - bit.ly/quaesita_saddest and bit.ly/quaesita_scientists

Podcast - bit.ly/dipod_saddest

## The leader/decision-maker’s role in statistics

If you’re responsible for calling the shots and you’re working with a statistician, what are your responsibilities? These blog posts reveal a few sides of * the part decision-makers play in the process*. Seeing the common thread is left as an exercise for the reader. (Hint: the statistician might help you get a good answer, but it’s up to you - the leader - to carefully frame your question. Otherwise, well, 42.)

Data science’s most misunderstood hero bit.ly/quaesita_hero

Statistics Savvy Self-Test bit.ly/quaesita_savvy

Incompetence, delegation, and population bit.ly/quaesita_incomp

Populations — You’re doing it wrong bit.ly/quaesita_popwrong

The saddest equation in data science bit.ly/quaesita_saddest

## Extra credit: A trick question for stats nerds

**“Bo did a hypothesis test. Correctly. If the ONLY other thing you knew about it was one of the following, which one gives you the most info?”**

**A) significance level = 0.08B) significance level = 0.05C) p-value = 0.1753D) p-value = 0.0032**

Read the answer here: bit.ly/quaesita_bo

**Is any of this available in other languages?**

It sure is, thanks to some amazing volunteers who’ve translated it for you. *(If you’d like to help translate to your language, please read this.) *

## That’s all for now

…Aaaaand that’s EVERYTHING I have published on statistics so far. Now in one place for your amusement. As always, if you enjoyed any of it, the loveliest way you can say thank you is to share it with someone who might enjoy it.

Next time, I’ll take you on a tour of a different data topic entirely…