```
library("vembedr")
library("htmltools")
```

For me, statistcal inference is a a tremendously powerful tool, but it requires you to respect all the rules lest the inference be lost. I was not trained as a statistician; for me (as you might expect), inference is an intimidating tool.

The idea of resampling is very appealing to me. I appreciate that you can answer (albeit somewhat less formally) many of the same questions as you can using inference. As long as you make a good-faith effort not to fool yourself, you can use resampling to get useful answers.

## References

This is clearly not a list of academic citations.

### Prof. Deborah Nolan

```
embed_user2016("Statistical-Thinking-in-a-Data-Science-Course") %>%
use_start_time("39m21s") %>%
div(align = "center")
```

One of the points that Prof. Nolan makes is that we can investigate the question of significance by using randomization, resampling, and rejection - rather than by using formal statistical inference.

She also refers to Tim’s Hesterberg’s paper on bootstraping and undergraduate statistics. Also refers to the Five Locks.

### Broom package

```
embed_user2016("broom-Converting-statistical-models-to-tidy-data-frames") %>%
div(align = "center")
```

GitHub repository

### R for Data Science

```
embed_user2016("Towards-a-grammar-of-interactive-graphics") %>%
use_start_time("15m15s") %>%
div(align = "center")
```

R for Data Science book

### Introduction to Statistical Learning

Link to the book