December 26, and there is no other correct answer.

I agree.

Thank you

Other: I took the inflatable shit down within a week, but left the lights up for another month because it helps my partner feel happy during the dark month of Jan.

It took me a long time to learn to think of the actual day of the holiday as the last day of that particular season of festivities. Deep down, I always felt it was like the center, with a couple weeks before and after still covered in decorations.

I notice when I'm out walking the dog that some of my neighbours still have led nets and such up and lit all day and night.

Could you please link to your source page directly, so it's obvious where you got your data? People here obviously will have questions about the methodology.

Also, what is the bin size on your chart?

(For example, seeing as there was no FDIC in 1934, and local banks would not have been on any kind of registry, I'm sure we'd all like to see whether the dataset attempts to do an adjustment for this or leaves the data raw. Also, the standard for a "bank failure" has changed significantly since the FDIC and associated regulations were established. In case people haven't noticed, SVB is still around and now solvent -- so is it a failure comparable to 1934?)

I don't even have the snow, but it's cold and I don't want to do it yet.

They're lucky the lights aren't plugged in anymore.

Did you check statistical significance? Humans are bad at eyeballing statistical significance, but it looks borderline to me.

> It also doesn’t seem to define the end of last year’s winter holidays

This probably isn’t a problem. This is a survey so people are reporting when they took the decorations down with reference to their own perception of when the holiday ended.

It’s just that the graph isn’t about the specific date that decorations are removed but how late people are comfortable leaving the decorations up with reference to when they view the end of the holiday occurred.

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Gosh darn it, maybe we should study pattern repeats in time! That’s a great idea! I wonder if I need an advanced physics or mathematics degree or if I can start with phi or pi… or the planets?

This is a bad title for the section.

The type of person who has an inflatable snowman is not the same as the typical person who puts up christmas lights and wreaths.

It also doesn't seem to define the end of last year's winter holidays which means some people are going to assuming very different dates for the start which is enough to mess up the week and month data slots.

Source: Grid News/The Harris Poll

Tool: Datawrapper

Article: How long is too long to leave up holiday decorations? What Americans really think about those St. Patrick’s skeletons.

This graph literally provides ample evidence of many bank failures across time that are not part of an identifiable wave.

Yup, I'm very aware - just making an observation

Looks like a dread sight of some kind

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Thanks for sharing. Interesting trend showing more parity.

Look how far down the safety schools Loyola and DePaul are lol

The y-axis is obvious, guys. Below the x-axis is the number of banks that failed and above the x-axis is the number of executives who benefitted from the failure.

Bank runs are confidence driven too. So occasional failures happen from incompetence or one-off circumstances. But when systemic confidence is threatened you get big clusters.

you need actual data for data to be beautiful. This graph has no meaning

I had been wondering if upsets were truly becoming more common as the years went by or did it simply feel that way. After 16 seed FDU's upset of top seeded Purdue last night I had to look into it. The number of upsets is simply the number of lower seeded teams who beat higher seeded teams in the first round of the tournament. The average seed difference is the seed of the higher seed subtracted from the lower seed and averaged for all upsets in a year (9 over 8 has a difference of 1, 12 over 5 has a difference of 7, etc.).

Since the Round of 64 began in 1985, both upsets and the average seed difference have been on the rise. For this visualization games between 8 and 9 seeds have been included even though the NCAA does not classify them as upsets. Had they not been included the broad trends still hold, with the average seed difference actually becoming more pronounced (6.7 average seed difference this year with 9 over 8 wins included, 9 average seed difference with those games excluded for example).

Source: NCAA archived brackets found on NCAA.com

Tools: Microsoft Excel

Now do an overlay showing government regulation and deregulation of the banking industry through time.

I think each dot is a bank that failed, yo!