Data Dojo Würzburg 35

DataDojo@Lunch - live

October 2025

Towards DataDojo 2.0

In the CCTB general assembly in April 2025, we decided to make some changes to the DataDojo to make it more valuable and fun. This is what we currently try:

So, as before, it will be hands-on and 90 minutes, so feel free to bring your lunch. But it will be more focused and with smaller groups.

Assign pairs

Currently a dumb version (does not take experience level and language preferences into account).

"Caro Sascha Axel Joana Felix Jannis Magdalena Markus"
⍉⬚""↯⊟2⌈÷2⊸⧻°⍆⊜□⊸≠@ 

Dataset

PoS - Significance, P values and t-tests

Please read Significance, P values and t-tests before the dojo.

Comprehension Questions

# BoxMuller from https://github.com/Omnikar/uiua-math
BoxMuller ← ∩×⤙⊓°∠∘×τ:√ׯ2°ₑ¬

μ ← ÷⊃⧻(/+)
σ ← √÷-1⊃⧻(/+)×.-⊸μ
𝒩 ← ◌ BoxMuller°⊟ gen :⚂⊟2

# Draw 10k normal samples of size n=5
≡𝒩 ⍥5 10000
μ≡σ

Tasks

Task 1 - why not use z for t? - or the difference between s and σ

Reproduce Supplementary Figure 4 image

  1. Simulate r=10_000 samples of size n=5 from a standard normal distribution (μ=0, σ=1). For each sample:
  2. Calculate the sample mean m
  3. Calculate the test statistic t using the sample standard deviation s: \(t = \frac{m}{s/\sqrt{n}}\)
  4. Calculate the test statistic z using the population standard deviation σ = 1 \(z = \frac{m}{\sigma/\sqrt{n}}\)
  5. Plot the distribution of t and z values
  6. (Optional) Plot lines for the standard normal distribution and the Student’s t distribution (with df=n-1=4)
  7. (Optional) What happens if you change n, r, σ, or μ?

Task 2 - does it matter for the p-value?

  1. As above, simulate r=10_000 samples of size n=5 from a standard normal distribution (μ=0, σ=1). For each sample:
  2. Calculate the t test statistic as above
  3. Calculate the p-value ($p_n$) as the probability to draw a more extreme value than t from a standard normal distribution
  4. Calculate the p-value ($p_t$) as the probability to draw a more extreme value than t from a Student’s t distribution (with df=n-1=4)
  5. Compare the fraction of $p_n$ and $p_t$ values that are below a certain threshold (e.g. 0.05)
  6. Repeat the above steps with different values for n(≥2)

Questions:

Getting started

Code to sample from a standard normal distribution and calculate sample means (m) and sample standard deviations (s):

# R
samples <- replicate(10000, rnorm(5))
m <- apply(samples, 2, mean)
s <- apply(samples, 2, sd)
# python
import numpy as np
samples = np.random.normal(size=(5, 10000))
m = samples.mean(axis=0)
s = samples.std(axis=0, ddof=1)
# julia
using Random, Distributions
samples = rand(Normal(), (5, 10000));
m = mean(eachcol(samples));
s = std(eachcol(samples))

Further ideas

Collaborative Tools and Workflow

Use your own device or CoCalc. Free choice of programming language, libraries, and tools.

Future Suggestions

Feel free to add suggestions to the list.

Points of Significance

Go through the papers of the Points of Significance series.

Points of View

Go through the papers of the Points of View series.

Medical Statistics

The Medical Statistics series consists of 14 reviews in the journal BMC Critical Care

Book club

Go through chapters of one of these books

Coding Dojo Katas

With a stronger focus on coding rather than data analysis, there is a nice collection of Katas