explain xkcd - User contributions [en]

2648: Chemicals

2022-07-22T12:39:04Z

162.158.62.151: added back removed statement; it looks to me like the citation actually supports keeping it. also removed sentence about DIY approaches being a poor investment, as they conjoin learning with production during hobby time, which is efficient.

{{comic
| number = 2648
| date = July 20, 2022
| title = Chemicals
| image = chemicals.png
| titletext = It's hard to believe, but lots of kids these days ONLY know how to buy prepackaged molecules.
}}

==Explanation==
{{incomplete|Created by BIG ISOMER - Please change this comment when editing this page. Do NOT delete this tag too soon.}}

In this comic, [[Megan]] mentions to [[Cueball]] that their company spends a lot on chemicals for which you can find formulas online. She suggests assembling chemicals from atoms "bought in bulk," holding a sheet of paper with the {{w|empirical formula}} C6H5NO2, which designates [https://pubchem.ncbi.nlm.nih.gov/#query=C6H5NO2 hundreds of compounds] including {{w|nitrobenzene}}, {{w|niacin}}, {{w|isonicotinic acid}}, and {{w|picolinic acid}}, followed by their component elements listed with prices. The ambiguity of chemical formulae is one of the jokes in the comic.

While many expensive chemicals are composed of inexpensive and easily available elements, "assembling" those elements into specific molecules is rarely as simple as Megan implies. That work is the primary purpose of the global chemical industry. In-house {{w|chemical synthesis}} is usually not cost effective, because end users have limited time and are generally unable to leverage the {{w|economies of scale}} inherent in bulk manufacturing by specialist industrial firms.[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2880393/] However, we don't know whether Megan and Cueball work in a laboratory, factory, or some other industrial setting. If they need chemicals in bulk, or only very small quantities, synthesizing them might be cost effective. In any case, producing chemicals from their constituent elements or {{w|Precursor (chemistry)|precursor compound}}s is difficult and time-consuming, usually requires expensive equipment, and is often fraught with peril.[https://www.youtube.com/watch?v=5QwW2owNWgc] {{w|Nitrobenzene}}, one of the C6H5NO2 compounds, is an excellent example because it is explosive and extremely toxic, and its synthesis is highly exothermic, making it one of the most dangerous syntheses in the chemical industry.[https://www.icheme.org/media/10339/xiii-paper-36.pdf] Such issues answer Cueball's question as to why more places don't manufacture their own compounds from atoms. Usually chemicals are derived from precursor chemicals instead of constituent elements. Megan seems to be imagining synthesis as a much simpler process without reactivity, energy release, or hazardous intermediate substances. The characters' naivety also gives rise to the humor of the comic.

Similar to the makerspace movement, community chemical labs have been cropping up, where people work together to perform chemical synthesis and other chemistry acts by sharing community resources.

"Big Molecule" is an [[2130: Industry Nicknames|industry nickname like Big Oil or Big Pharma]], amusing in its own right, and conceivably implying that the chemical industry is conspiring to prevent end users from synthesizing their own compounds. Big Oil and Big Pharma are real industrial nicknames; with "Big Pharma", huge corporations invest in research and then charge immensely high prices for medicines. This became more notable when the coronavirus vaccine was released, with media and commerce being focused on large pharmaceutical manufacturers rather than community labs, where community labs could possibly have distributed vaccines directly to their surrounding communities, and recruited from these communities to aid manufacturing, if supported by their governments and economies. Comparatively, "Big Molecule" is fictitious.

Megan is holding a note that shows how many of the four type of atoms she needs to build one of those she wish to assemble. It seems like the paper lists prices for buying 6 carbon, 5 hydrogen, 1 nitrogen and 2 oxygen atoms. Although the (very small?) prices cannot be read. But the numbers are for certain from the formulae, so the $ sign must refer to the unreadable bit coming after. Also she sums the number of atoms up, to find that she only needs 14, but still with an unreadable price sum to the right of the number of atoms. She thus suggest buying atoms in bulk, which should be even cheaper than buying them individually. However, this is another layer of humor, as you can neither buy individual atoms or get a price for them. Just showing her complete lack of understanding of chemistry.

The title text refers to the fact that older people often complain that "kids these days" don't know how to do things that seemed fundamental to past generations. Randall may have expressed that he dislikes other statements like these in [[2165: Millennials|previous comics]]. It may also refer to the decline of home {{w|chemistry set}}s popular from the late 1700s through the early 1980s that encouraged kids to experiment with basic chemical reactions like generating esters or polymers, or the even older decline in home manufacture of gunpowder as was common in the 1800s. Chemical engineering was more widely practiced during the development of plastics, but far fewer people understand how they are made today. Similarly with automobiles, domesticated crops, and many other technologies that progressed through a period of popular attention but became siloed into industries, corporations, governments, or branches of academia. This is happening now with some software, circuitry, and other technologies, where fewer people know how to build and troubleshoot complex devices and systems. Technology users thus lose their ability to build and repair machines and modify their tools themselves, having to rely on paid services instead.

This comic may have been prompted by recent news that [https://english.elpais.com/science-tech/2022-07-15/for-the-first-time-in-history-we-can-modify-atomic-bonds-in-a-single-molecule.html scientists have found a way to assemble and change atoms in individual molecules] by modifying their bonds.

==Transcript==
:[Megan and Cueball standing next to each other. Megan has her palms raised.]
:Megan: You know how our company spends a lot on expensive chemicals?
:Cueball: Yeah?

:[Zoom in on Megan who is holding piece of paper up in one hand. The paper has a large chemical formula at the top. Below is a list of the atoms needed, with amount and a price tag in dollars but with unreadable amount. There is a sum total at the bottom beneath a line.]
:Megan: Well, I just learned you can look up all of the formulas online!
:Megan: We can just buy the atoms in bulk and assemble them here!
:Paper:
::<big>C6H5NO2</big>
::Carbon 6 $...
::Hydrogen 5 $...
::Nitrogen 1 $...
::Oxygen 2 $...
::Total 14 $...

:[Cueball is now on the left of Megan as she is walking past him to the right holding her arms outstretched with her palms up.]
:Cueball: I wonder why more places don't do that.
:Megan: People have no idea they're getting ripped off by Big Molecule!

{{comic discussion}}

[[Category:Comics featuring Megan]]
[[Category:Comics featuring Cueball]]
[[Category:Chemistry]]

2399: 2020 Election Map

2021-02-09T18:19:21Z

162.158.62.151:

{{comic
| number = 2399
| date = December 16, 2020
| title = 2020 Election Map
| image = 2020_election_map.png
| titletext = There are more Trump voters in California than Texas, more Biden voters in Texas than New York, more Trump voters in New York than Ohio, more Biden voters in Ohio than Massachusetts, more Trump voters in Massachusetts than Mississippi, and more Biden voters in Mississippi than Vermont.
}}

*A [https://xkcd.com/2399/large/ larger version] of this image can be found by clicking the image at xkcd.com - the comic's page can also be accessed by clicking on the comic number above.

==Explanation==
This is a sequel to an earlier comic, [[1939: 2016 Election Map]]. The United States elects its president not directly by popular vote but by an Electoral College composed of a number of electors, partially proportional to population, from each state. Presently, a "winner-take-all" system is used in most states: the winner of the popular vote in each state receives all of the electoral votes for that state. Though, strictly speaking, the electors are not required to cast their ballots according to this system, many states impose penalties on them if they don't. Technically, the popular vote in each state is to elect a slate of electors who in turn elect the President.

During the election season, news outlets and other political trackers tend to color-code each state with the party which won the state (or which is projected or speculated to win). Since the 2000 election it's become common practice to code Republican victories as red and Democratic victories as blue. Other parties have less consistent colors, but are commonly green. These colors have become embedded in popular vernacular, as states that are heavily Republican are known as "red states" and those that are heavily Democratic are known as "blue states".

These graphics can be misleading as to the realities on the ground, though. Because each state is colored solidly red or blue, it gives the impression that each state belongs entirely to one party or the other, when the color could represent a very slender minority, or an overwhelming advantage. In addition, such a graphical view means that larger states translate to more area of a given color, giving the impression of party strength, even though that may not represent many voters. In the US, large cities trend largely Democratic, while rural areas trend largely Republican. This means that many Democratic voters are concentrated in relatively small urban areas, so a large "red" state may represent fewer voters than a small "blue" state.

Randall's solution to this is to represent the Republican and Democratic voters in each state with Cueball icons, each icon representing 250,000 voters. He has made some attempt to distribute the Cueball icons within a state in a manner similar to how the actual votes were distributed. This has the advantage of giving a decent impression of how popular each candidate was, how their popularity varied across the country, and how the votes were distributed by both state and region. It also gives at least a basic indication of population patterns in the US, with large regions that are sparsely inhabited, and populations clustered in urban centers.

The title text compares different voter pools in terms of absolute size. These facts are frequently counter-intuitive. California is generally thought of as a "blue state", and Texas as a "red state", so it's surprising to realize that, in 2020, Donald Trump received more votes in California than he did in Texas. The reason for this is not complex, California has a huge population, nearly 40 million people, of whom 17.5 million voted in 2020. Even though Joe Biden won the state easily, Trump received 6 million of those votes. Texas, by contrast, has 27.7 million residents and 11.3 million voters in the 2020 election. Trump received 5.9 million of those votes, which was enough to win the state. Because of the huge variation in population among US states, and the political divisions within each state, there are multiple "blue" states which have more Republican voters that at least some "red" states, and vice versa. This underscores the importance of not viewing any state as politically uniform. Even if a state trends heavily toward one party, there is always a substantial population of the other party, and in large states, that means enough people that they'd be a formidable political force anywhere else.

{| class="wikitable"
!|
!colspan="2"|Biden/Harris
!colspan="2"|Trump/Pence
!rowspan="2"|Total
Votes
|-
!|State
!|Votes
!|Perc
!|Votes
!|Perc
|-
|California
|style="text-align: right;"|11,110,250
|style="text-align: right;"|63.48%
|style="text-align: right; background-color: yellow;"|6,006,429
|style="text-align: right;"|34.32%
|style="text-align: right;"|17,500,881
|-
|Texas
|style="text-align: right; background-color: lightgreen;"|5,259,126
|style="text-align: right;"|46.48%
|style="text-align: right; background-color: yellow;"|5,890,347
|style="text-align: right;"|52.06%
|style="text-align: right;"|11,315,056
|-
|New York
|style="text-align: right; background-color: lightgreen;"|5,230,985
|style="text-align: right;"|60.86%
|style="text-align: right; background-color: dodgerblue"|3,244,798
|style="text-align: right;"|37.75%
|style="text-align: right;"|8,594,826
|-
|Ohio
|style="text-align: right; background-color: lightsalmon;"|2,679,165
|style="text-align: right;"|45.24%
|style="text-align: right; background-color: dodgerblue"|3,154,834
|style="text-align: right;"|53.27%
|style="text-align: right;"|5,922,202
|-
|Massachusetts
|style="text-align: right; background-color: lightsalmon;"|2,382,202
|style="text-align: right;"|65.60%
|style="text-align: right; background-color: chartreuse;"|1,167,202
|style="text-align: right;"|32.14%
|style="text-align: right;"|3,631,402
|-
|Mississippi
|style="text-align: right; background-color: aqua;"|539,508
|style="text-align: right;"|41.06%
|style="text-align: right; background-color: chartreuse;"|756,789
|style="text-align: right;"|57.60%
|style="text-align: right;"|1,313,894
|-
|Vermont
|style="text-align: right; background-color: aqua;"|242,820
|style="text-align: right;"|66.09%
|style="text-align: right;"|112,704
|style="text-align: right;"|30.67%
|style="text-align: right;"|367,428
|}
{{w|2020_United_States_presidential_election#Results_by_state|Source}}

The following table lists the number of 250,000-vote markers in the map by candidate and state, and compares this with the actual number of votes. {{w|2020_United_States_presidential_election#Results_by_state|Source}}
{| class="wikitable"
!|
!colspan="3"|Biden/Harris
!colspan="3"|Trump/Pence
!colspan="3"|Others
!colspan="3"|Total
|-
!|State
!|Markers
!|Votes
!|Actual votes
!|Markers
!|Votes
!|Actual votes
!|Markers
!|Votes
!|Actual votes
!|Markers
!|Votes
!|Actual votes
|-
|Alabama (AL)
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|849,624
|style="text-align: right;"|6
|style="text-align: right;"|1,500,000
|style="text-align: right;"|1,441,170
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|32,488
|style="text-align: right;"|9
|style="text-align: right;"|2,250,000
|style="text-align: right;"|2,323,282
|-
|Alaska (AK)
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|153,778
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|189,951
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|15,801
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|359,530
|-
|Arizona (AZ)
|style="text-align: right;"|7
|style="text-align: right;"|1,750,000
|style="text-align: right;"|1,672,143
|style="text-align: right;"|7
|style="text-align: right;"|1,750,000
|style="text-align: right;"|1,661,686
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|53,497
|style="text-align: right;"|14
|style="text-align: right;"|3,500,000
|style="text-align: right;"|3,387,326
|-
|Arkansas (AR)
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|423,932
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|760,647
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|34490
|style="text-align: right;"|5
|style="text-align: right;"|1,250,000
|style="text-align: right;"|1,219,069
|-
|California (CA)
|style="text-align: right;"|44
|style="text-align: right;"|11,000,000
|style="text-align: right;"|11,110,250
|style="text-align: right;"|24
|style="text-align: right;"|6,000,000
|style="text-align: right;"|6,006,429
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|384,202
|style="text-align: right;"|70
|style="text-align: right;"|17,500,000
|style="text-align: right;"|17,500,881
|-
|Colorado (CO)
|style="text-align: right;"|7
|style="text-align: right;"|1,750,000
|style="text-align: right;"|1,804,352
|style="text-align: right;"|5
|style="text-align: right;"|1,250,000
|style="text-align: right;"|1,364,607
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|88,021
|style="text-align: right;"|13
|style="text-align: right;"|3,250,000
|style="text-align: right;"|3,256,980
|-
|Connecticut (CT)
|style="text-align: right;"|4.5
|style="text-align: right;"|1,125,000
|style="text-align: right;"|1,080,680
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|715,291
|style="text-align: right;"|0.5
|style="text-align: right;"|125,000
|style="text-align: right;"|28,309
|style="text-align: right;"|8
|style="text-align: right;"|2,000,000
|style="text-align: right;"|1,824,280
|-
|Delaware (DE)
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|296,268
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|200,603
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|7,475
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|504,346
|-
|District of Columbia (DC)
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|317,323
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|18,586
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|8,447
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|344,356
|-
|Florida (FL)
|style="text-align: right;"|21
|style="text-align: right;"|5,250,000
|style="text-align: right;"|5,297,045
|style="text-align: right;"|23
|style="text-align: right;"|5,750,000
|style="text-align: right;"|5,668,731
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|101,680
|style="text-align: right;"|45
|style="text-align: right;"|11,250,000
|style="text-align: right;"|11,067,456
|-
|Georgia (GA)
|style="text-align: right;"|10
|style="text-align: right;"|2,500,000
|style="text-align: right;"|2,473,633
|style="text-align: right;"|10
|style="text-align: right;"|2,500,000
|style="text-align: right;"|2,461,854
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|62,229
|style="text-align: right;"|20
|style="text-align: right;"|5,000,000
|style="text-align: right;"|4,997,716
|-
|Hawaii (HI)
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|366,130
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|196,864
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|11,475
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|574,469
|-
|Idaho (ID)
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|287,021
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|554,119
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|26,874
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|868,014
|-
|Illinois (IL)
|style="text-align: right;"|14
|style="text-align: right;"|3,500,000
|style="text-align: right;"|3,471,915
|style="text-align: right;"|10
|style="text-align: right;"|2,500,000
|style="text-align: right;"|2,446,891
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|114,938
|style="text-align: right;"|25
|style="text-align: right;"|6,250,000
|style="text-align: right;"|6,033,744
|-
|Indiana (IN)
|style="text-align: right;"|5
|style="text-align: right;"|1,250,000
|style="text-align: right;"|1,242,416
|style="text-align: right;"|7
|style="text-align: right;"|1,750,000
|style="text-align: right;"|1,729,519
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|61,183
|style="text-align: right;"|12
|style="text-align: right;"|3,000,000
|style="text-align: right;"|3,033,118
|-
|Iowa (IA)
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|759,061
|style="text-align: right;"|4
|style="text-align: right;"|1,000,000
|style="text-align: right;"|897,672
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|34,138
|style="text-align: right;"|7
|style="text-align: right;"|1,750,000
|style="text-align: right;"|1,690,871
|-
|Kansas (KS)
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|570,323
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|771,406
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|30,574
|style="text-align: right;"|5
|style="text-align: right;"|1,250,000
|style="text-align: right;"|1,372,303
|-
|Kentucky (KY)
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|772,474
|style="text-align: right;"|6
|style="text-align: right;"|1,500,000
|style="text-align: right;"|1,326,646
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|37,648
|style="text-align: right;"|9
|style="text-align: right;"|2,250,000
|style="text-align: right;"|2,136,768
|-
|Louisiana (LA)
|style="text-align: right;"|4
|style="text-align: right;"|1,000,000
|style="text-align: right;"|856,034
|style="text-align: right;"|5
|style="text-align: right;"|1,250,000
|style="text-align: right;"|1,255,776
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|36,252
|style="text-align: right;"|9
|style="text-align: right;"|2,250,000
|style="text-align: right;"|2,148,062
|-
|Maine (ME)
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|435,072
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|360,737
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|23,652
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|819,461
|-
|Maryland (MD)
|style="text-align: right;"|8
|style="text-align: right;"|2,000,000
|style="text-align: right;"|1,985,023
|style="text-align: right;"|4
|style="text-align: right;"|1,000,000
|style="text-align: right;"|976,414
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|75,593
|style="text-align: right;"|12
|style="text-align: right;"|3,000,000
|style="text-align: right;"|3,037,030
|-
|Massachusetts (MA)
|style="text-align: right;"|10
|style="text-align: right;"|2,500,000
|style="text-align: right;"|2,382,202
|style="text-align: right;"|4
|style="text-align: right;"|1,000,000
|style="text-align: right;"|1,167,202
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|81,998
|style="text-align: right;"|15
|style="text-align: right;"|3,750,000
|style="text-align: right;"|3,631,402
|-
|Michigan (MI)
|style="text-align: right;"|11
|style="text-align: right;"|2,750,000
|style="text-align: right;"|2,804,040
|style="text-align: right;"|9
|style="text-align: right;"|2,250,000
|style="text-align: right;"|2,649,852
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|85,410
|style="text-align: right;"|21
|style="text-align: right;"|5,250,000
|style="text-align: right;"|5,539,302
|-
|Minnesota (MN)
|style="text-align: right;"|7
|style="text-align: right;"|1,750,000
|style="text-align: right;"|1,717,077
|style="text-align: right;"|6
|style="text-align: right;"|1,500,000
|style="text-align: right;"|1,484,065
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|76,029
|style="text-align: right;"|13
|style="text-align: right;"|3,250,000
|style="text-align: right;"|3,277,171
|-
|Mississippi (MS)
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|539,508
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|756,789
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|17,597
|style="text-align: right;"|5
|style="text-align: right;"|1,250,000
|style="text-align: right;"|1,313,894
|-
|Missouri (MO)
|style="text-align: right;"|5
|style="text-align: right;"|1,250,000
|style="text-align: right;"|1,253,014
|style="text-align: right;"|7
|style="text-align: right;"|1,750,000
|style="text-align: right;"|1,718,736
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|54,212
|style="text-align: right;"|12
|style="text-align: right;"|3,000,000
|style="text-align: right;"|3,025,962
|-
|Montana (MT)
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|244,786
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|343,602
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|15,286
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|603,674
|-
|Nebraska (NE)
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|374,583
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|556,846
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|25,044
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|956,383
|-
|Nevada (NV)
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|703,486
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|669,890
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|32,000
|style="text-align: right;"|6
|style="text-align: right;"|1,500,000
|style="text-align: right;"|1,405,376
|-
|New Hampshire (NH)
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|424,937
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|365,660
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|13,236
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|790,597
|-
|New Jersey (NJ)
|style="text-align: right;"|9
|style="text-align: right;"|2,250,000
|style="text-align: right;"|2,608,335
|style="text-align: right;"|7
|style="text-align: right;"|1,750,000
|style="text-align: right;"|1,833,274
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|57,744
|style="text-align: right;"|16
|style="text-align: right;"|4,000,000
|style="text-align: right;"|4,549,353
|-
|New Mexico (NM)
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|501,614
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|401,894
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|20,457
|style="text-align: right;"|4
|style="text-align: right;"|1,000,000
|style="text-align: right;"|923,965
|-
|New York (NY)
|style="text-align: right;"|20.5
|style="text-align: right;"|5,125,000
|style="text-align: right;"|5,230,985
|style="text-align: right;"|13
|style="text-align: right;"|3,250,000
|style="text-align: right;"|3,244,798
|style="text-align: right;"|0.5
|style="text-align: right;"|125,000
|style="text-align: right;"|119,043
|style="text-align: right;"|34
|style="text-align: right;"|8,500,000
|style="text-align: right;"|8,594,826
|-
|North Carolina (NC)
|style="text-align: right;"|11
|style="text-align: right;"|2,750,000
|style="text-align: right;"|2,684,292
|style="text-align: right;"|11
|style="text-align: right;"|2,750,000
|style="text-align: right;"|2,758,775
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|81,737
|style="text-align: right;"|22
|style="text-align: right;"|5,500,000
|style="text-align: right;"|5,524,804
|-
|North Dakota (ND)
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|114,902
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|235,595
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|11,322
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|361,819
|-
|Ohio (OH)
|style="text-align: right;"|11
|style="text-align: right;"|2,750,000
|style="text-align: right;"|2,679,165
|style="text-align: right;"|12
|style="text-align: right;"|3,000,000
|style="text-align: right;"|3,154,834
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|88,203
|style="text-align: right;"|24
|style="text-align: right;"|6,000,000
|style="text-align: right;"|5,922,202
|-
|Oklahoma (OK)
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|503,890
|style="text-align: right;"|4
|style="text-align: right;"|1,000,000
|style="text-align: right;"|1,020,280
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|36,529
|style="text-align: right;"|6
|style="text-align: right;"|1,500,000
|style="text-align: right;"|1,560,699
|-
|Oregon (OR)
|style="text-align: right;"|5
|style="text-align: right;"|1,250,000
|style="text-align: right;"|1,340,383
|style="text-align: right;"|4
|style="text-align: right;"|1,000,000
|style="text-align: right;"|958,448
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|75,490
|style="text-align: right;"|10
|style="text-align: right;"|2,500,000
|style="text-align: right;"|2,374,321
|-
|Pennsylvania (PA)
|style="text-align: right;"|15
|style="text-align: right;"|3,750,000
|style="text-align: right;"|3,458,229
|style="text-align: right;"|14
|style="text-align: right;"|3,500,000
|style="text-align: right;"|3,377,674
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|79,380
|style="text-align: right;"|29
|style="text-align: right;"|7,250,000
|style="text-align: right;"|6,915,283
|-
|Rhode Island (RI)
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|307,486
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|199,922
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|10,349
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|517,757
|-
|South Carolina (SC)
|style="text-align: right;"|4
|style="text-align: right;"|1,000,000
|style="text-align: right;"|1,091,541
|style="text-align: right;"|6
|style="text-align: right;"|1,500,000
|style="text-align: right;"|1,385,103
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|36,685
|style="text-align: right;"|10
|style="text-align: right;"|2,500,000
|style="text-align: right;"|2,513,329
|-
|South Dakota (SD)
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|150,471
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|261,043
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|11,095
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|422,609
|-
|Tennessee (TN)
|style="text-align: right;"|5
|style="text-align: right;"|1,250,000
|style="text-align: right;"|1,143,711
|style="text-align: right;"|7
|style="text-align: right;"|1,750,000
|style="text-align: right;"|1,852,475
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|57,665
|style="text-align: right;"|12
|style="text-align: right;"|3,000,000
|style="text-align: right;"|3,053,851
|-
|Texas (TX)
|style="text-align: right;"|21
|style="text-align: right;"|5,250,000
|style="text-align: right;"|5,259,126
|style="text-align: right;"|24
|style="text-align: right;"|6,000,000
|style="text-align: right;"|5,890,347
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|165,583
|style="text-align: right;"|46
|style="text-align: right;"|11,500,000
|style="text-align: right;"|11,315,056
|-
|Utah (UT)
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|560,282
|style="text-align: right;"|4
|style="text-align: right;"|1,000,000
|style="text-align: right;"|865,140
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|62,867
|style="text-align: right;"|6
|style="text-align: right;"|1,500,000
|style="text-align: right;"|1,488,289
|-
|Vermont (VT)
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|242,820
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|112,704
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|3,608
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|367,428
|-
|Virginia (VA)
|style="text-align: right;"|10
|style="text-align: right;"|2,500,000
|style="text-align: right;"|2,413,568
|style="text-align: right;"|8
|style="text-align: right;"|2,000,000
|style="text-align: right;"|1,962,430
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|84,526
|style="text-align: right;"|18
|style="text-align: right;"|4,500,000
|style="text-align: right;"|4,460,524
|-
|Washington (WA)
|style="text-align: right;"|9
|style="text-align: right;"|2,250,000
|style="text-align: right;"|2,369,612
|style="text-align: right;"|6
|style="text-align: right;"|1,500,000
|style="text-align: right;"|1,584,651
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|133,368
|style="text-align: right;"|16
|style="text-align: right;"|4,000,000
|style="text-align: right;"|4,087,631
|-
|West Virginia (WV)
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|235,984
|style="text-align: right;"|2
|style="text-align: right;"|500,000
|style="text-align: right;"|545,382
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|13,365
|style="text-align: right;"|3
|style="text-align: right;"|750,000
|style="text-align: right;"|794,731
|-
|Wisconsin (WI)
|style="text-align: right;"|7
|style="text-align: right;"|1,750,000
|style="text-align: right;"|1,630,866
|style="text-align: right;"|6
|style="text-align: right;"|1,500,000
|style="text-align: right;"|1,610,184
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|56,991
|style="text-align: right;"|13
|style="text-align: right;"|3,250,000
|style="text-align: right;"|3,298,041
|-
|Wyoming (WY)
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|73,491
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|193,559
|style="text-align: right;"|0
|style="text-align: right;"|0
|style="text-align: right;"|9,715
|style="text-align: right;"|1
|style="text-align: right;"|250,000
|style="text-align: right;"|276,765
|-
|Total
|style="text-align: right;"|325
|style="text-align: right;"|81,250,000
|style="text-align: right;"|81,268,867
|style="text-align: right;"|296
|style="text-align: right;"|74,000,000
|style="text-align: right;"|74,216,747
|style="text-align: right;"|12
|style="text-align: right;"|3,000,000
|style="text-align: right;"|2,896,077
|style="text-align: right;"|633
|style="text-align: right;"|158,250,000
|style="text-align: right;"|158,381,691
|}

==Transcript==

:2020 Election Map
:[One stick figure in a black circle] = 250,000 votes
:[Stick figure in a blue circle] Biden
:[Stick figure in a red circle] Trump
:[Stick figure in a green circle] Other
:Votes are distributed by state as accurately as possible while keeping national totals correct.
:Location within each state is approximate.
:[Blue, red, and green circles are distributed across a map of the United States.]

{{comic discussion}}
[[Category:Politics]]
[[Category:Elections]]
[[Category:Comics with color]]
[[Category:Large drawings]]
[[Category:Comics featuring politicians]]
[[Category:Maps]]
[[Category:Comics featuring Donald Trump]]

2348: Boat Puzzle

2020-08-23T20:06:32Z

162.158.62.151: /* Reasonable Assumptions */

{{comic
| number = 2348
| date = August 19, 2020
| title = Boat Puzzle
| image = boat_puzzle.png
| titletext = 'No, my cabbage moths have already started laying eggs in them! Send the trolley into the river!' 'No, the sailing wolf will steal the boat to rescue them!'
}}

==Explanation==
This comic is a twist on {{w|Wolf, goat and cabbage problem|an old riddle}}. In the original riddle, a person has to cross a river in a boat that can only hold them and one other object. They have a wolf, a goat, and a cabbage that they need to bring across with them, similar to the first panel. If the wolf is left alone with the goat, however, the wolf will eat the goat; and if the goat and cabbage are alone, the goat will eat the cabbage. (The problem can be solved in seven trips.)

However, the comic quickly devolves into surrealism in the later panels as new characters show up, bringing deviations of the original "cabbage", "goat", and "wolf" that add extra layers of complexity to the riddle. White Hat brings extra wolves and cabbages. Black Hat, in his traditional classhole style, brings {{w|cabbage moth}}s which will infest unsupervised cabbages with destructive larvae, and boat-destroying {{w|termite}}s. How he intends to bring them across the river (or even if he wants to) is unknown, but it brings to mind the parable of {{w|The Scorpion and the Frog}}. Beret Guy arrives with a wolf who can operate a boat, who could perhaps serve as a second pilot to expedite the crossing, so long as he is not asked to ferry a goat, and also a goat who eats wolves (which does not alter the problem constraints but is unusual, as one would expect from Beret Guy's associate).

The last panel is a reference to the {{w|Trolley_Problem|Trolley Problem}}, a moral test that asks the participant whether they would passively let people in the way of an uncontrollable trolley die or actively divert the trolley to kill a single person standing on a branch of the tracks. The comic gives a twist here too: according to the title text, the characters must choose between stopping the trolley full of wolves with a cushion of cabbages (in which Black Hat's cabbage moths have laid eggs, which he implicitly argues are morally equivalent to "innocent children") or letting it crash into the river (at which point the wolf who can operate a boat will steal the boat to rescue the wolves from the trolley, which will delay the other characters from crossing the river).

The River Crossing puzzle was also mentioned in [[1134: Logic Boat]] and referenced in [[589: Designated Drivers]].

The Trolley Problem was also mentioned in [[1455: Trolley Problem]] and referenced in [[1938: Meltdown and Spectre]].

==Solving The Problem==

Unlike typical Logic Boat problems the presence of multiple humans makes finding a solution almost trivial, however trying to determine the solution with the least number of trips could still make the somewhat challenging. Because the set of constraints are both ambiguous and incomplete, it requires the reader to make assumptions that, in turn, will lead to different solutions.

===Reasonable Assumptions===

The following assumptions can be made based on the setup of the problem or are necessary to avoid an unsolvable puzzle.

* '''Cueball is an observer.''' He is set up as an observer there to solve the problem, not pilot the boat or "watch" the cargo.
* '''The boat can only hold two items.''' This is standard in logic boat problems. Groups of insects count as one item.
* '''Black Hat and Beret Guy both want to cross the river with their cargo'''. Neither states that they wish to cross the river like Ponytail and White Hat, but it can be inferred from the setup of the scenario.
* '''The termites will destroy the boat ''after'' crossing.''' Otherwise the problem is unsolvable. This is similar to the {{w|Poncelet–Steiner theorem}}, which shows that any compass-and-straightedge construction can be completed with a "rusty compass", so long as the compass works at least once.
* '''The wolf-eating goat also eats cabbage.''' The wolf-eating constraint adds to the goat's existing constraints.
* '''The sailing wolf follows the command of an adjacent human.''' The alternatives require more assumptions for a solvable puzzle.
* '''The sailing wolf returns the trolley wolves to the near shore.''' The trolley wolves show no indication of wanting to cross the river.
* '''Stopping the trolley destroys all the cabbages.''' Otherwise the event does not affect the logic puzzle.
* '''The pack of wolves from the trolley will eat a human or wolf-eating goat left by themselves.''' Aligns with the spirit of the constraints.
** '''A wolf can protect a human from a pack of wolves'''. A human who is accompanied by one of the wolves who want to cross the river should not count as being "alone" for the purpose of getting eaten by the wolves from the trolley; otherwise, there is no way to get everyone across.

===The Trolley===

The trolley problem creates two versions of the puzzle, one where the cabbages are destroyed, the other where they are not and a wolf rescue takes place. The ethical issues associated with the trolley problem are independent from the logic of how to cross the river.

===Solutions===

* General

With four humans involved, the first trip across can bring an extra human who then can guard the cargo as it is brought across in arbitrary order with care being taken not to have predator and prey along together at the end. The termites must be last cargo ferried across as they will destroy the boat.

* The Trolley is Stopped

The cabbages are destroyed. The second to last trip brings across the last human and the last trip brings across the termites.

* The Trolley is not Stopped

A pack of wolves is now on the near bank. The last human is brought across in the third to last trip, followed by the last wolf and lastly the termites.

===Missing Information===

No information is provided about whether or not the humans all get along with each other and this is left as a possible exercise for the reader given all of the characters' varying personality traits. However the sailing wolf would likely come in handy if certain humans (ex Black Hat, Beret Guy) cannot be left alone. It is also probable that certain characters might not serve in the capacity as a cargo guard.

It is also unclear if humans can leave with their cargo once all the cargo has been brought across. This could complicate matters if a far side "guard" leaves early.

==Transcript==
:[Cueball and Ponytail are standing on the bank of a river. There is a boat in the river. A goat and wolf are also on the riverbank, and Ponytail is holding a cabbage.]
:Ponytail: I need to cross the river. I have a wolf, a goat, and a cabbage.
:Cueball: Hmm.

:[White Hat appears, accompanied by two wolves and pulling a wagon full of cabbages.]
:Cueball: OK, here's what-
:White Hat: Hi, I also need to cross. I have two wolves and 100 cabbages.

:[Black Hat arrives, surrounded by a cloud of flying creatures and carrying a jar of bugs under his arm. Beret Guy follows with another wolf and goat on leashes.]
:Black Hat: I have 50 cabbage moths and 2,000 boat-destroying termites.
:Beret Guy: I have a wolf that can operate a boat, and a goat that eats wolves.

:[The fourth panel is a zoomed-out shot, where everything but the sky appears black.]

:[A trolley speeds in, leaving a trail of dust in its wake. A person is standing on the front, and many ears are barely visible above the seats.]

:Cueball: Hang on, I need to make a spreadsheet.
:Trolley operator: Look out!
:Trolley operator: My wolf-filled trolley is out of control and can only be stopped by a cushion of cabbages!

{{comic discussion}}
[[Category:Comics featuring Cueball]]
[[Category:Comics featuring Ponytail]]
[[Category:Comics featuring White Hat]]
[[Category:Comics featuring Black Hat]]
[[Category:Comics featuring Beret Guy]]
[[Category:Logic]]
[[Category:Animals]]
[[Category:Food]]

277: Long Light

2019-10-27T16:16:19Z

162.158.62.151: /* Explanation */.jpg

{{comic
| number = 277
| date = June 15, 2007
| title = Long Light
| image = long_light.png
| titletext = You can look at practically any part of anything manmade around you and think "some engineer was frustrated while designing this." It's a little human connection.
}}

==Explanation==
This strip depicts a common experience to most people - becoming frustrated with a device, system, or rule that appears to be badly made or have no purpose other than to frustrate the user (in this case, a traffic light that seems unreasonably, inexplicably long). One temptation we might have in these cases is to blame the designer of the system. Here, the designer appears and testifies to the amount of effort that went into the design, considering many factors. He challenges [[Cueball]] to come up with a better solution, the implication being that without a similar amount of training and effort, any naive solution would have flaws the designer would be happy to point out. This demonstrates to Cueball ''and'' the reader that just because they were unlucky enough to encounter something in a way that was inconvenient for no obvious reason, doesn't mean there is no reason at all.

Of course, all of this has occurred after the designer leapt out of nowhere onto the hood of the car, so he may not be entirely stable. This is elaborated upon in the final panel, where the designer finally admits that red light won't change until Tuesday (presumably a day or more beyond the current time), meaning that the timing scheme really was absurd after all. It is also possible that the designer has intentionally changed the light specifically to make Cueball wait for a couple of days, or to stop himself from being flung off of the hood of the car. This comic was published on a Friday; it seems Cueball has to wait a few days more.

The title text returns to the original point, reminding us that designers work hard and often encounter complex problems in doing their jobs. Their frustration may also be in part from the knowledge that future users will blame them for unavoidable problems and undervalue their work. With a little empathy, we can find a human connection to these problems, rather than let them drive us crazy.

Randall returns to the theme of the unseen contributions of engineers in [[1741: Work]].

==Transcript==
:[Cueball in a car, sitting at a red light.]
:Cueball: This light always takes forever. I'd like to smack the idiot who designed this intersection.
:[An engineer steps up onto the hood of Cueball's car.]
:Engineer: Hi.
:Cueball: Who the hell are you?
:Engineer: I designed this intersection.
:Engineer [arms spread outward]: You're right - I should have just made the light shorter! Never mind the hours of simulation and testing I did. Never mind that this intersection interacts with it's neighbors in a complicated way and it took me a week to work out timing sequences that avoided total jams.
:Engineer: Clearly, I'm a crappy engineer and you have a better solution. Go on, show me your proposed timings.
:Cueball: Get the hell off my hood before I start driving and fling you into traffic.
:Engineer: You can't. Light's red.
:Cueball: Well, when will it change?
:Engineer: Tuesday.

{{comic discussion}}
[[Category:Comics featuring Cueball]]
[[Category:Traffic light]]
[[Category:Comics with color]]

Talk:245: Floor Tiles

2019-10-24T12:51:24Z

162.158.62.151:

I wond what HE thinks:
http://www.youtube.com/watch?v=i5Jyu6eioZ4

;Obsession
The explanation fails to mention the link with Obsessive-compulsive disorder (OCD). Stepping on, or avoiding particular floor tiles can be an example of OCD-induced behaviour. Moreover OCD is positively correlated with high IQ and therefore within the "nerd" scope of XKCD.

The reason why Cueball simply denies walking funny (sic) instead of offering the correct explanation, apart from simplicity, may also be because he is embarrassed by the awareness of his own disorder.--[[Special:Contributions/158.169.131.14|158.169.131.14]] 10:03, 5 September 2012 (UTC)

Personally, I have this walking obsession too. Curiously, my friends can understand my algorithm after observing me in my natural habitat for 2 minutes.

:(New editor, here, the above line being someone else unsigned, or somehow divorced from a prior signing.)
:While I'm bigger with longer legs now, I'm pretty sure my naturally long stride arose from nurturing a regular gait that would avoid cracks in awkwardly-sized paving slabs.
*....*....*....* Original natural gait
(Let's call that 100% of normal, for the sake of comparison, based purely on this ASCII.)

|---|---|---|---|---| Slabs
*...*...*...*...* Shortened gait, typically centered on one step per slab
(80% stride. Obviously shortened and somewhat awkward.)

|---|---|---|---|---| Slabs
*.......*.......* Too-long gait, typically centered on one step ''every other'' slab
(160% stride. ''Very'' akward)

|---|---|---|---|---| Slabs
*.....*.....*.....* Slightly lengthened gait, two slabs, miss one, repeat.
(120% stride. Obviously longer, but not too awkward once practiced. )

:The 120% version (or whatever proportion it is, IRL) effectively works by aiming the feet just after a crack, foot just before a crack, skipping the next slab entirely.
:That tactic was developed back when I was <10 years old (but the latent mathematician in me is proud of my younger self's approach to analysing the tiling), possibly back when I was 6 or 7. They were large rectangular slabs, meaning different strategies in different directions. But when shorter that might mean the above applied when travelling in the 'short direction', but 'long direction' walking was two steps per one slab (after crack, before next crack), one on the next (middle). As I grew up a bit it would have become 'every other slab' in the short-axis, and the above in the long axis. Growing up yet more and it changes again. Diagonal travel is finessed accordingly.
:As an adult, most newer paving slabs have tended to changed from the traditional large rectangular ones to smaller square ones, just big enough for feet to fit on without any possibility of finessing that. Luckily they tend to be monochrome, so it's just a whole number stepped over (when I notice what's under my feet enough to have to do something about it), sometimes involving a Pythagorean calculation... ;) [[Special:Contributions/178.98.31.27|178.98.31.27]] 03:30, 21 June 2013 (UTC)

;Sidewalk cracks

My edit/observation that the title text alludes to an {{w|Old wives' tale}} "if you step on a crack you will break your mother's back" was removed. I'm just hoping for additional discussion. "Sidewalk cracks ... out-of-sync with your natural stride" seems to be a clear reference to the wives' tale in question. As I reread my edit I realize this doesn't explain Cueball's behavior, so I was wrong on that point, but my assertion that the title text ''does'' point to the wives' tale seems valid enough. Thoughts?
--[[User:Smartin|Smartin]] ([[User talk:Smartin|talk]]) 00:10, 3 January 2013 (UTC)

: Additional: I am confounded why Wikipedia does not recognize "step on a crack" as a wives' tale, childrens' taunt, etc. Is there some [http://en.wiktionary.org/wiki/pondian pondian] aspect to this I am missing? In the Midwestern US where I grew up, "step on a crack" is (or was) a common meme; it was even exposited in [http://www.metrolyrics.com/whip-it-lyrics-devo.html song] (sorry for the lame lyric link) --[[User:Smartin|Smartin]] ([[User talk:Smartin|talk]]) 04:51, 3 January 2013 (UTC)

:It's not so much referencing the step on a crack and break your mother's back child's game so much as many of those with obsessive compulsive tendencies are really annoyed that each footstep cannot be classified as a digital step, meaning that your foot is only one one tile at a time. It's not so much that you don't want to "break your mother's back" it's just a matter of personal pride that you don't walk on cracks. Same problem with steps that are just barely too long that you always end up taking the next one with the same foot. You just start to feel off kilter. [[User:Lcarsos|lcarsos]]_a ([[User talk:Lcarsos|talk]]) 07:52, 3 January 2013 (UTC)

:Never heard of the "mother's back" version. Though I'd do this for (referencing prior section title) probably vaguely OCD reasons without an actual mythology behind it, whenever there was a reason for it (e.g. in children's picture books) it was always something to do with bears. (Here in the UK, that is, without any unzooed bears roaming around. i.e. obviously ficticious ones.) [[Special:Contributions/178.98.31.27|178.98.31.27]] 02:59, 21 June 2013 (UTC)

::http://www.dltk-kids.com/crafts/miscellaneous/mmilne-linesandsquares.htm

I feel we should have some mention of comic 207: what xkcd means here, and vice versa {{unsigned ip|108.162.214.15}}
: I agree, but in 207 it wasn't really referencing this; this had not been created yet. I think we should remove the reference note. [[Special:Contributions/108.162.237.231|108.162.237.231]] 23:56, 21 April 2014 (UTC) ( Classhole forgot to log in. )

'''Alternative explanation'''

The '''main''' reason he does not explain is embarrassment. In the first panel, he is being accused of walking funny. He considers explaining the reason for the behavior but realizes the explanation is even more embarrassing than the behavior itself. He quickly ends the exchange by stating, "I'm not walking funny.", thereby avoiding further embarrassment. {{unsigned|Flewk}}

Why does the explanation say the tile map is incorrect? The tile pattern looks fine to me, it’s just not rotated. Am I just being dumb?

Talk:2217: 53 Cards

2019-10-19T17:37:27Z

162.158.62.151: Nope

"This page was last edited [tomorrow]." Okay, good to know. Tomorrow starts three hours from now, my time. This comic reminded me of this article: https://arstechnica.com/science/2019/10/corkscrewing-bouncy-ion-drive-would-provide-thrust-in-different-universe/ [[Special:Contributions/172.68.38.88|172.68.38.88]] 00:44, 19 October 2019 (UTC)

I can do this, but my flowchart would be different and involve secretly inserting a joker, using the shuffling as cover for the move.
Collect a deck of 52 cards and have a spectator count the cards. - Secretly hide a joker from the deck in your off-hand (the one without the deck). - Shuffle the cards, letting the hidden card drop on top of the deck. - Keep shuffling, so the inserted joker is well mixed into the deck. - Have a spectator count the cards, looking only at the backs. - 53.
[[User:Nutster|Nutster]] ([[User talk:Nutster|talk]]) 04:56, 19 October 2019 (UTC)

Actually this is also what encryption scientists have to face talking to not so few encryption enthusiasts who just invented their own encryption method[[Special:Contributions/162.158.234.112|162.158.234.112]] 07:01, 19 October 2019 (UTC)
:Ohg V unir na haornnoyr pvcure! [[Special:Contributions/162.158.158.253|162.158.158.253]] 13:52, 19 October 2019 (UTC)

Wait, isn't perpetual motion (w.r.t. a inertial reference frame) possible, at least according to Newtonian mechanics? Just launch something into space at high enough speed and "watch" it wander away forever. Extracting (an unbounded amount of) energy from that object is a totally different story... --[[Special:Contributions/162.158.234.94|162.158.234.94]] 10:11, 19 October 2019 (UTC)
:Not really, as even in vacuums particles randomly come into existence. Eventually enough would be in the path to slow it to a stop. [[Special:Contributions/162.158.62.151|162.158.62.151]] 17:37, 19 October 2019 (UTC)

Getting a 53 card deck from a 52 card deck is easy. First, cut the deck twice. Then, shuffle all parts together; be sure to suffer thoroughly. Finally, take off the top 5 cards, sneak in the Joker on the bottom while nobody's looking, and put the five cards at the "middle". Because of skewed philosophy, you will have gotten a 53 card deck![[Special:Contributions/162.158.122.186|162.158.122.186]]

2210: College Athletes

2019-10-02T01:19:55Z

162.158.62.151:

{{comic
| number = 2210
| date = October 2, 2019
| title = College Athletes
| image = college_athletes.png
| titletext = Their signature play is the three-point combinator, a recursive offense which is guaranteed not to halt and continues accumulating points until the buzzer.
}}

==Explanation==
{{incomplete|Created by a Steph Curry. Please mention here why this explanation isn't complete. Do NOT delete this tag too soon.}}

In this comic, Cueball claims that all members of his college basketball team changed their name to Steph Curry, after the NBA Player of the same name. In particular, one player copied the name from the NBA player, then another member of the team copied the name from that player, and so on.

This process is called "Currying", a play on both the name "Curry" used here, as well as the mathematical procedure called {{w|currying}}, wherein a multivariable function is broken down into a sequence of single-variable functions, each of which outputs a new function until the final variable is consumed. For example, the function f(x,y,z) can be curried into f(x)(y)(z), where f is a function that consumes x and produces a function f(x), which in turn consumes y, yielding the function f(x)(y), and that in turn is a function f(x)(y) which consumes the parameter z to finally produce f(x)(y)(z), which is equal to the original f(x,y,z). This is not commonly used in most areas of math except for foundational logic and pure functional programming.

==Transcript==
{{incomplete transcript|Do NOT delete this tag too soon.}}

Ponytail: Oh, huh. California passed a law giving college athletes full rights to their names and images.

White Hat: Good, I think?

Cueball: That's nothing. OUR state gave college players rights to use the names and images of ANY California athletes.

Ponytail: It did not.

Cueball: Sure it did! That's how our school fielded a basketball team made up entirely of Steph Currys.

White Hat: Or is the plural "Stephs Curry?"

Cueball: They didn't copy the original Steph, though. One player got the rights to his name, then the next player got it from them, and so on. This process is known as "Currying."

Ponytail: ...I hate you so much.

{{comic discussion}}