Data Practices: Introduction

For years, the dominant model used in many parts of science education revolved around teaching the scientific method.  This was presented as a somewhat linear process, wherein the scientist would:

1. Define a purpose;
2. Construct a hypothesis;
3. Test the hypothesis and collect data;
4. Analyze data;
5. Draw conclusions; and
6. Communicate results. (see https://examples.yourdictionary.com/scientific-method-examples.html Links to an external site. )

While these steps remain important, inquiry-based education recognizes that there are many different practices that scientists engage in, and that these are not necessarily practiced linearly.  At the core of any scientific endeavor is data.  If a scientist's data is generated poorly, then their results may be misleading or uninterpretable.  Likewise, scientists need to be able to work with, clean up, and interpret data in order to conduct sound scientific inquiry.  For the purposes of this grant, everything we do will center around five data practices.  Namely:

1. creating data
2. collecting data
3. manipulating data
4. analyzing data, and
5. visualizing data.