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In the process of designing my Snellen Eye Chart typographical posters, I came across the Snellen font by Andrew Howlett. I wasn't happy with all the letters, so I made attempts at giving the font an update. I call this redesign "Snellen MK", to avoid conflict with Howlett's "Snellen".

Not being a font designer, I will likely get myself into trouble.

While making my Snellen chart series, I entered the rabbit hole of optotype fonts ... and I can't get out!

The charts don't necessarily use the latest version of my Snellen font design, which fluctuates as my mood about some of the letters changes.

The optotype requirement is that letters be designed on a 5 × 5 grid, and have constant stroke width. This means that both lower and upper case letters need to share the grid and stroke. To stay compatible with the eyechart paradigm, letters should be as obvious as possible.

Lorrie Frear's article What are Optotypes? Eye Charts in Focus is a great read about optotypes and eye charts.

The uppercase letter design uses Herman Snellen's original chart as inspiration.

I have modified the design by Andrew Howlett (see below) for some letters. All the changes are relatively minor: more serifs and consistent stroke width for bars on R and K.

The lowercase characters should be considered experimental.

The progress of my redesign is shown below. I would **greatly appreciate feedback and suggestions!**

The distribution contains both Andrew's version and my redesign.

v7.002 11-Jul-2019 — Download SnellenMK optotype font

Tidied all letter forms with Fontlab 6.

Fixed g and e. Thanks to Makeesha Fisher for suggestions.

Adjusted serifs on f, j, l, o, t to extend the full width of the grid. Added a lot more symbols.

Added lowercase, digits and symbols.

Adding digits.

I'm exploring the lowercase characters. I don't know what I want to do with them. Make this into a more standard font in which lowercase letters are smaller, so that letters can fit their roles clearly when text is set in sentence case, or fill out the full optotype grid.

Flushed out some inconsistencies in the uppercase characters. Added serifs to more letters.

Now all the letters occuppy the full 5 × 5 grid, including the I, whose serifs were widened to allow this. While this new uppercase I isn't as pretty as the old one, it makes the entire typeface more consistent to its optotype roots.

Still struggling with the G. In the original version, the descending stroke was cut off in the middle of a grid, which I didn't like.

The S has been fixed—thanks to Elanor Lutz for feedback.

I've color coded the characters slightly differently, drawing attention to ones that I feel need more thought.

The lowercase characters aren't color coded (yet) because ... most of them need help. Primarily, I'm vacillating between making them fill the full size of the 5 × 5 square, just like the uppercase characters, and keeping them confined to a 4 × 4 square, which incurs loss of legibility. If I make the letters the same size, it will be impossible to distinguish lowercase and uppercase characters some cases (e.g. c, i). Perhaps this is desired?

First attempt at lowercase characters.

*You can look back there to explain things,
but the explanation disappears.
You'll never find it there.
Things are not explained by the past.
They're explained by what happens now.
—Alan Watts*

A Markov chain is a probabilistic model that is used to model how a system changes over time as a series of transitions between states. Each transition is assigned a probability that defines the chance of the system changing from one state to another.

Together with the states, these transitions probabilities define a stochastic model with the Markov property: transition probabilities only depend on the current stateâ€”the future is independent of the past if the present is known.

Once the transition probabilities are defined in matrix form, it is easy to predict the distribution of future states of the system. We cover concepts of aperiodicity, irreducibility, limiting and stationary distributions and absorption.

This column is the first part of a series and pairs particularly well with Alan Watts and Blond:ish.

Grewal, J., Krzywinski, M. & Altman, N. (2019) Points of significance: Markov Chains. *Nature Methods* **16**:663–664.

*Places to go and nobody to see.*

Exquisitely detailed maps of places on the Moon, comets and asteroids in the Solar System and stars, deep-sky objects and exoplanets in the northern and southern sky. All maps are zoomable.

Quantile regression explores the effect of one or more predictors on quantiles of the response. It can answer questions such as "What is the weight of 90% of individuals of a given height?"

Unlike in traditional mean regression methods, no assumptions about the distribution of the response are required, which makes it practical, robust and amenable to skewed distributions.

Quantile regression is also very useful when extremes are interesting or when the response variance varies with the predictors.

Das, K., Krzywinski, M. & Altman, N. (2019) Points of significance: Quantile regression. *Nature Methods* **16**:451–452.

Altman, N. & Krzywinski, M. (2015) Points of significance: Simple linear regression. *Nature Methods* **12**:999–1000.

Outliers can degrade the fit of linear regression models when the estimation is performed using the ordinary least squares. The impact of outliers can be mitigated with methods that provide robust inference and greater reliability in the presence of anomalous values.

We discuss MM-estimation and show how it can be used to keep your fitting sane and reliable.

Greco, L., Luta, G., Krzywinski, M. & Altman, N. (2019) Points of significance: Analyzing outliers: Robust methods to the rescue. *Nature Methods* **16**:275–276.

Altman, N. & Krzywinski, M. (2016) Points of significance: Analyzing outliers: Influential or nuisance. Nature Methods 13:281–282.

Two-level factorial experiments, in which all combinations of multiple factor levels are used, efficiently estimate factor effects and detect interactionsâ€”desirable statistical qualities that can provide deep insight into a system.

They offer two benefits over the widely used one-factor-at-a-time (OFAT) experiments: efficiency and ability to detect interactions.

Since the number of factor combinations can quickly increase, one approach is to model only some of the factorial effects using empirically-validated assumptions of effect sparsity and effect hierarchy. Effect sparsity tells us that in factorial experiments most of the factorial terms are likely to be unimportant. Effect hierarchy tells us that low-order terms (e.g. main effects) tend to be larger than higher-order terms (e.g. two-factor or three-factor interactions).

Smucker, B., Krzywinski, M. & Altman, N. (2019) Points of significance: Two-level factorial experiments *Nature Methods* **16**:211–212.

Krzywinski, M. & Altman, N. (2014) Points of significance: Designing comparative experiments.. Nature Methods 11:597–598.