Category Archives: CHI

citation not always needed (via XKCD)

Why I Don’t Write a “Related Work” Section

Imagine someone explaining a complex topic, like how to improve the fuel efficiency of a boat. But shortly after starting the explanation, they go off on a series of tangents about pretty boats they’ve seen, big boats, rubber ducks, submarines, and other transportation vehicles such as the new 787 by Boeing et al. Then they return to the explanation of boat efficiency without ever referencing why they brought up those strange tangents.

Tangents are confusing, and they hurt clarity. The related work section is often just a string of unrelated tangents, which is a waste of the reader’s time.

Now let me make something clear: I am not necessarily saying that papers should cite fewer sources. Instead, each citation should serve an obvious, specific purpose. And if that purpose is so tangential to the structure of your argument that you need to put it in what amounts to a citation dumping ground, then it isn’t needed.

What’s the purpose of a citation?

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Mysterious Origins of Hypotheses in Visualization and CHI

For years, I’ve noticed a strange practice in Visualization and CHI. When describing a study, many papers list a series of predictions and number them as H1, H2, H3… For example:

  • H1: Red graphs are better than blue graphs
  • H2: Participants will read vertical bar graphs more quickly than horizontal bar graphs

I have never seen this practice in any other field, and I was curious as to the origin.

Half Hypotheses

Although these statements are referred to as ‘hypotheses’, they’re not… at least, not completely. They are predictions. The distinction is subtle but important. Here’s the scientific definition of hypothesis according to The National Academy of Sciences:

A tentative explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation…

The key word here is explanation. A hypothesis is not simply a guess about the result of an experiment. It is a proposed explanation that can predict the outcome of an experiment. A hypothesis has two components: (1) an explanation and (2) a prediction. A prediction simply isn’t useful on its own. If I flip a coin and correctly guess “heads”, it doesn’t tell me anything other than that I made a lucky guess. A hypothesis would be: the coin is unevenly weighted, so it is far more likely to land heads-up. It has an explanation (uneven weighting) that allows for a prediction (frequently landing heads-up).

The Origin of H1, H2, H3…

Besides the unusual use of the term “hypothesis”, where does the numbering style come from? It appears in many IEEE InfoVis and ACM CHI papers going back to at least 1996 (maybe earlier?). However, I’ve never seen it in psychology or social science journals. The best candidate I can think of for the origin of this numbering is a misunderstanding of null hypothesis testing, which can be best explained with an example. Here is a null hypothesis with two alternative hypotheses:

  • H0: Objects do not affect each other’s motion (null hypothesis)
  • H1: Objects attract each other, so a ball should fall towards the Earth
  • H2: Objects repel each other, so a ball should fly away from the Earth

Notice that the hypotheses are mutually exclusive, meaning only one can be true. In contrast, Vis/CHI-style hypotheses are each independent, and all or none of them can be true. I’m not sure how one came to be transformed into the other, but it’s my best guess for the origins.

Unclear

On top of my concerns about diction or utility, referring to statements by number hurts clarity. Repeatedly scrolling back and forth trying to remember “which one was H3 again?” makes reading frustrating and unnecessarily effortful. It’s a bad practice to label variables in code as var1 and var2. Why should it be better to refer to written concepts numerically? Let’s put an end to these numbered half-hypotheses in Vis and CHI.

Do you agree with this perspective and proposed origin? Can you find an example of this H numbering from before 1996? Or in another field?

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