# Data traps

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> [!NOTE]
> **Estimated time:** 1.5 hours

## Learning objectives

In this module, you will learn to:

- Investigate potential issues underlying raw or processed datasets, including collection and quality issues.
- Identify biases, invalid inferences, and rationalizations.
- Find common issues in data analysis, including correlation, relatedness, and irrelevance.
- Examine a chart for common problems, misperceptions, and misleading display and design choices.

## ML motivation

While not as glamorous as model architectures and other downstream model work,
data exploration, documentation, and preprocessing are critical to
ML work. ML practitioners can fall into what Nithya Sambasivan et al. called
[data cascades](https://research.google/blog/data-cascades-in-machine-learning/)
in their [2021 ACM paper](https://dl.acm.org/doi/10.1145/3411764.3445518)
if they do not deeply understand:

- the conditions under which their data is collected
- the quality, characteristics, and limitations of the data
- what the data can and can't show

It's very expensive to train models on bad data and
only find out at the point of low-quality outputs that there were problems
with the data. Likewise, a failure to grasp the limitations of data, human
biases in collecting data, or mistaking correlation for causation,
can result in over-promising and under-delivering results, which can lead to a
loss of trust.

This course walks through common but subtle data traps that ML and data
practitioners may encounter in their work.