Category Archives: Science

InfoVis 2014 – The methods papers

This year, 40% of InfoVis papers included an empirical evaluation. I made a list in my last post.

There were also a couple papers worth noting that described methods for evaluating visualizations. These papers can help bootstrap future evaluations, leading to a better understanding of when and why vis techniques are effective.

Learning Perceptual Kernels for Visualization Design – Çağatay Demiralp, Michael Bernstein, Jeffrey Heer pdf
A collection of methods are described to find the relative discriminability of feature values (e.g. colors or shapes). It also looks at finding the descriminability of combinations of visual features (e.g. colors and shapes). The paper validates its approach by determining the discriminability of size and showing which of their measures closely match the established Steven’s power law for size.

A Principled Way of Assessing Visualization Literacy – Jeremy Boy, Ronald Rensink, Enrico Bertini, Jean-Daniel Fekete pdf
This paper describes how to use Item Response Theory – a technique common in psychometrics and education literature – to assess a person’s “literacy” or skill with visualizations. I would have liked to have seen the approach validated (or at least compared) with some external factor like the person’s experience with visualization. Understandably, that can be tough to measure, but this method certainly shows promise for explaining individual differences in user performance.

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?