Charts Are Like Pasta – Data Visualization Part 1: Crash Course Statistics #5

Today we’re going to start our two-part unit on data visualization. Up to this point we’ve discussed raw data – which are just numbers – but usually it’s much more useful to represent this information with charts and graphs. There are two types of data we encounter, categorical and quantitative data, and they likewise require different types of visualizations. Today we’ll focus on bar charts, pie charts, pictographs, and histograms and show you what they can and cannot tell us about their underlying data as well as some of the ways they can be misused to misinform.

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31 replies on “Charts Are Like Pasta – Data Visualization Part 1: Crash Course Statistics #5”

Dear Adriene,
Thanks for the superb content
I have some doubts
-if mean is so sensitive to outliers and such a bad representation of the overall data why is it most frequently used measure? Wouldnt it make more sense to use median/mode?
-Is not standard deviation just a "mean"? then also like three measures of central tendency (mean, mode and median) are there three measures of spread as well 1)similar to mean – standard deviation
2) similar to mode – ?????
3)simular to median – ?????

Does the statistic on the likelyhood to die suffocating on food assume that death is imminent within the given year? Should the probability not be a lot smaller considering the amount of people who DON'T die during a given year?

How do only 10% of your family think statistics are scary?
Probably because those 10% are the only ones who learned statistics in school. Just like the guy who put a sliced avocado with its core on a sandwich…

Just posting my notes of whatever I could remember after seeing the video once.
Making this a study habit – watch the full video and note what you recollect.
Can be used as reference. Will be doing this for every video.
Feedback appreciated.

Course 5: Data Visualizations Part 1

We have 2 types of data – Quantitative and Categorical
Quantitative Data is numeric and has a meaningful order and is equally spaced e.g. ounces of olive oil per U.S. home
Categorical Data is non-numeric and is not ordered or equally spaced e.g. Which type of pasta is most liked by people ?
Categorical data can be displayed using a frequency or a relative frequency table (for better comparison)
Frequency table can have more than one variables (e.g. white sauce/red sauce pasta)
Categorical data can also be displayed as bar charts (with one of more variables like frequency table)
It can also be represented by a Pictograph (though increasing the size of the pictures does not give us a better comparison)
It can also be represented as a pie chars (as our eyes are pretty good at comparing pie slices)
Always keep an eye on the axes while reading a visualization
Quantitative variables can also be displayed as categorical variables by binning(categorizing them in groups)
Point to keep in mind is to see the bins are appropriate and are not obscuring the data
A bar chart showing quantitative binned data is continuous (unlike categorical data bar chart) as there is no distinctions and the bins are continuous
Such a chart is called as a histogram

I love these series! I am studying Psychology and I get so excited watching these videos because they are so insightful and logical. Your explanations are really good, thank you so much!

I would understand if the amount of olive oil is excessive if it's not being used. Then you would have to consider how many people are living there, and how many flatmates you have. I live at a 5 bedroom area with a big family, so the largest amount you've described is suitable for our family. Then if we had guests we would need more olive oil than most. Also, considering the fact that my siblings cook and bake, that's an expected amount.

"pie charts are useful because our eyes are good at comparing slices" This is actually the absolute opposite of the truth. At this point in academic science, it's essentially recommended to just not use pie charts at all, because we're really bad at parsing them correctly

Minor detail – Histograms depict value of the data not by height by the area of the corresponding bar since you can have histograms consisting of bars with unequal width. For example in cases of histograms made for continuous data which is binned into bins of unequal sizes (like age groups)

Ahhh. I failed a Stat course in the university, I had to resit it: I didn't know it could be this interesting, but I always knew it was important.

This is the second time I'm watching these set of videos.

I see that the key for most people is to make the matter relatable. Not just learning about Standard deviation— I see its usefulness in the real world.

Thanks Crash Course!

I think Crash Course is the best thing that happened to Youtube to be honest. This is such a useful channel! I use it to review before my exams or when I need to review material that I learned in the past. Also I was able to find answers to the questions that I had stuck in my head for months. I like how quickly and simply the information is delivered and simple examples are used to describe the concepts. Thank you so much!

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