Really want to make a difference in the world? Well, read on to find out about how some scientists are literally saving lives, just by analyzing data.
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Why is understanding data types so important, and why can it be difficult? When is a number categorical? Why are dates problematic?
Having data organized by variable, and knowing what type each variable is, is very important, as it guides (or limits) the mathematical tests you can perform.
Thinking about data types before performing an experiment can make the work afterwards a lot easier!
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The DataClassroom interactive T-test is designed to give a visual, intuitive understanding of what the test is actually doing, all the way from looking at the mean values of your data to generating, and interpreting, the P-value that is the result.
This post describes the various pedagogical details in the design, and the thinking behind them.
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We know it can be difficult getting on top of the important data analysis phase of an experiment. You’ve got a load of measurements, but how to arrange them? Maybe you’ve read our articles on Tidy Data - this helps - but now there is another tool to assist you.
By running a DataClassroom simulation, you can nail down what your experimental variables are, think more deeply about what you are measuring, and try out visualizations and ways to analyze - all before you go to the lab!
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Let’s say you want to know if a treatment or category affects an outcome. Could be anything. Does a new pill reduce bloodpressure? Are left-handed people more athletic?
There is an answer, but it is hidden inside the bodies of the subjects. How to get that answer out of them?
And when experimenting, how do you ensure you can get a significant result?
We know this can be hard to teach, and DataClassroom Simulations can help.
I’ve always thought there was something inherently attractive about the normal distribution, aka the “bell curve”.
But how many samples do you need to get that nice smooth curve?
And what does that tell us about how we evaluate significance of results?
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Magic is not always great. Especially in the learning process, it’s not a good thing if finished results just pop out of a tool - like magic!
But were our Graph-Driven Tests maybe doing too much behind the scenes? And what did we do about it?
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The R Project is fantastic. With it, you can do virtually any data visualization or analysis task - and it is free and open source.
But it’s probably not controversial to say that learning to code in R can be pretty intimidating for many.
Our Bridge to R feature provides scaffolding to give learners a great starting point.
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There is a misconception that students should use spreadsheets for everything - because “that’s what they’ll be using when they are in the workplace”.
But scientists don’t generally use spreadsheets for visualization and analysis. And there’s a reason for that.
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Two new features we’d like to tell you about today:
Plotting residuals (and slopes) from regression lines
Plotting a reference normal curve on histograms, and testing for normality.
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Sometimes your data need a bit of tweaking. Not to falsify your results (of course!) but maybe they need to be in different units of measurement, or you want to convert a linear measurement into an area.
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We just added a whole bunch of new color scales to the app, so you can color-map your graphs with even more color schemes with spicy names like Magma and Viridis…
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We’re pleased to announce the launch of our new online User Guide!
And a new feature - the ability to customize which controls appear on the user interface, so the app can appear simpler, or more advanced.
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It’s not that all bar graphs are bad. However, when not used carefully, bar graphs have the potential to unnecessarily hinder a student in learning to interpret data through graphs. With data from classroom experiments at the middle or high school level, we often want students to compare averages of some measured variable across groups. What are the best graphing options for this situation?
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How can a digital tool support a student as they begin to work with their own data? It’s all very well to just provide the features they’ll need, but what exactly can a tool assist with at the data import and preparation stages?
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Descriptive statistics, like mean, median and error bars, illustrate specific aspects of data, and are valuable in drawing conclusions or making comparisons.
We have focused on making these easily available, and easy to play with and to switch between the various forms, to encourage the student to think more about which story their data is telling them.
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My 10th grade biology students had just completed the classic heart-rate/exercise lab and I was left wondering why it fell so short of the learning experience I had planned for them. Despite the great data that my students collected, I had failed yet again to budget sufficient time to really dive into the data analysis with the class.
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By graphing the data in a way that illustrates the question, for example “Are these groups significantly different?”, the student has already done the groundwork.
When they then click on the Graph-Driven Test button, the tool uses this information to suggest an appropriate test.
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The Graph Wizard is an optional assistant for students that could use help in choosing and setting up an appropriate graph for their data.
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We know that this is an extremely difficult moment in education as we are all scrambling to figure out what learning looks like for students during weeks to months-long Covid-19 school closures. We already offer free 90-day trials of DataClassroom and will extend that for any affected school or family for as long as this thing lasts.
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