In OP's chart the problem is more the scale than the start point, but it's always about context.

I think the difference in time of day between the two outlined areas to be intriguing as well. The time of day is split up in 4 hour intervals starting at 0600 for Morning.

The calving cycle sees a higher number of collisions between 1800 and 2200 whereas the mating cycle has a higher collision rate during the afternoon hours 1400-1800.

I would be interested to see how that compares to sunset times, but I’m not quite sure how to visualize that on top of what I already have here.

I agree that it can be used to mislead but that isn't always the case.

Take disposable income. Straight from Fred. https://fred.stlouisfed.org/series/DSPIC96

If you click on "view last 5 years" your Y axis is going to start way above zero. It just makes sense. If you click on "view max" you will get Y axis closer to zero because the range of values justifies it.

Of course it can be manipulated. As I said, it can be OK if the units are different or if the series have different standard deviations.

In every case it's important for the reader to look at the axes and draw their own conclusions.

I guess the larger lesson is Do Your Own Research.

This is really cool. As someone who lives in Wisconsin, I can attest that it's well known in the area to use extreme caution when driving in November.

Actually, I will try and add one more thing to present more constructive criticism:

If you included an example of data being misrepresented by both options, I think you would solve the issue of misleading people into thinking certain plotting practices are intrinsically misleading. So, for instance, if you showed that data can be distorted by truncating a bar graph, but also that data can be distorted by NOT truncating a bar graph, I think you would make a far more valid argument about how to analyze graphical data skeptically.

Okay, I said what I came here to say. There is nothing special about the examples you selected. If a user encounters, for instance, a bar chart that's been truncated not to start at zero, it's no more likely that this has been done for legitimate reasons than it is that it's been done for illegitimate ones. Similarly, it's just as likely that a bar chart which begins at zero had it's axis selected to mislead about the data as it is that is has it starting at zero to accurately represent the data. Flagging one of those options as potentially misleading is itself a potentially misleading statement.

If you feel like you need to get the last word in here, feel free. I think I've presented the best form of my argument so I am done now.

Now I know haha

These examples can be used to mislead and the purpose is to show to users how it can be done so the next time users sees truncated bar chart on TV, maybe they will think more carefuly before making judgment about visually represented data.

Data was acquired from the Wisconsin Department of Transportation. The data was wrangled and cleaned in RStudio where it was also visualized.

I have finished my first class in a Data Science Program and decided to put my newfound skills to the test.

You appear to have maliciously misspelled Hanlon's Razor.

Good luck! Try super subtle background colour, sort out the labels, no boxes needed probs, etc. I also recommend Tufte’s book on data vis!

Okay, if you don't actually believe that these are practices that are more likely to be used to mislead than to accurately inform, then what is your justification for labeling them as misleading practices?

One of the most common misunderstandings I dealt with when I was doing STEM education with people reading graphs is when the data are presented non-linearly. If you present people with, for instance, a logarithmic graph it's much more likely they will get the wrong impression of the data. But I would never consider log graphs to be misleading. It seems to me like you are doing something analogous here.

which ones ?

I agree with you and those scenarios are probably more common, but it seems like it would be incredibly hard to quantify that, so it’s susceptible to cognitive biases.

I cannot support it with data nor did I claim they are more often on purpose. Sometimes it is just a bad design and different programmes have different default settings for labels and axis.

Haha did they scale the height and totally ignore the massive surface increase?

That's why it's relative :)

[removed]

Or, in Musk's case, if they have enough dickriders and market manipulators to spike his stock price to the point where none of his businesses actually have to turn a profit because he can simply continue securing loan after loan like every other rich asshole, kiting these loans around.

The ignorance of the truth is fucking astounding.

You used the # symbol which makes it big and bold

Read The Visual Design of Quantitative Information by Tufte

That's not the only situation. Trend lines are graphs that are used to show...well.. the trends, and the absolute quantities are not as important in many cases.

Stock prices from a certain year are a good example. It's not that it doesn't have meaning, the price of the stock is valuable information, it's just not as important as the trend and depending on the amounts it could make the trend hard to read

I use 2 all the damn time, because it's very frequently necessary.

Like, I'm trying to show the strong correlation between Current(A) and Motor RPM. My Current values range from 8-15, and my rpm ranges from 10,000-18,000. I'm absolutely scaling or normalizing them so the correlation between the two is visually clear.

What if you make the Y axis negative, so something that looks bigger is actually smaller?