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Hi, from your writing it is clear to me that you don't really understand the difference between different data types such as continuous, discrete, categorical, or which types of charts are best to show each type (such as line chart, box plot, histogram). That is fine, but obvs. I won't go and do your research for you, and I don't know how much time you want to spend on this. I think you'll need to Google a bit until you find a site that explains the basic ideas of the most common chart types and then you'll figure out which one suits your situation best.
Your specific example: education rate per 1000 VS average wage. You use the term "compare" again. I think you may be trying to say you are interested in their relationship. If that's correct, why not try a line chart with one variable on the x-axis and another on the y-axis?
Statistical tests are not use to summarise. They are used to test hypotheses.
You could have 10,000 boys and 10,000 girls and you measure their SAT scores in English and Maths. Then you have 2x2x10.000 data points. Probably too much to plot. So you summarize them into 4 numbers: average English and Maths scores for the boys and girls. Those 4 numbers are the summaries.
" just can't get around how to compare to seemingly completely unrelated bits of data"
Data are not related or unrelated to each other, they are just the numbers representing phenomena. It is the phenomena which may or may not be related, but even if they are related, a statistical test may not be able to detect that (or a statistical test may claim that they are related even though they are not). For example, check out these highly correlated but totally independent data:
https://www.tylervigen.com/spurious-correlations