Great art makes you wonder,
great design makes things clear.
John Maeda, in ‘The laws of simplicity’
Post Microsoft
10 years ago Microsoft software was dominant in my usage – Windows, Office, Messenger, IE, and probably more.
Today, the only Microsoft product that I use is Visual Studio Code (I switched from Sublime Text last year).
I haven’t used Windows, IE or messenger in a decade. I do occasionally use Excel and Skype, when someone insists, but have neither installed on my devices.
Decision making – the empty quadrant
The diagram in the previous post had an empty quadrant. It bugged me that I could not think of a decision making process that lay in that quadrant.
Which decision making process considers lots of options, and votes on them (or discusses them) regularly?
It came to me next morning. And once it came, it stayed. It’s so obvious that there are books, and cartoon strips, and TV sitcom episodes based on it.
Decision making – the Parliament, general elections, and referendums
Here’s a basic principle of decision making:
If a decision is critical and not easily reversible, consider all options deeply.
If a decision is easily amendable, make a quick decision and revisit frequently.
General election voting uses the first principle. Parliaments vote using the second. The Brexit referendum was an illogical mix of the two, causing a biased outcome.
Premier league table 2018-19 – some trends so far
Time to revisit the excellent BBC chart from last year. Here’s how things stand in the premier league after 21 matches:
Observations (relative to last year):
- There’s no runaway winner at the top,
- The middle is again crowded (8 teams within 9 points) but not as much as last year (13 teams in 11 points),
- The bottom 6 are scattered as well, with Huddersfield struggling the most (more on them below).
aditya
Douglas Adams, on Confirmation Bias
See first, think later, then test.
But always see first. Otherwise you will only see what you were expecting.
— Wonko the sane, in ‘So long, and thanks for all the fish’
Commenting – AVC, Disqus, privacy, and WordPress
This is what greeted me when I tried to comment on Fred Wilson’s post today.
Fred Wilson wrote about signing up to Pocket, and requested suggestions for becoming a power user. Naturally, I wanted to comment with a plug for my Chrome extension for Pocket. I also wanted to offer my 2c on why I found Instapaper better than Pocket 1.
I didn’t. Continue reading Commenting – AVC, Disqus, privacy, and WordPress
Availability bias and the remote work advantage
Removal of the easiest to observe input metric – face time – reduces the availability bias in remote work organisations, and helps them focus on the more productive outcome-based metrics.
This switch to emphasis on outcomes can be helpful for individual productivity, but is truly transformative when the whole organisation goes remote-first.
Behavioural biases confuse performance appraisal in office-based organisation culture
The time spent in office looking productive is a key factor in performance appraisals across teams and organisations. Even when time in office is not a formal factor, it unconsciously creeps in and affects rating scores on other factors.
This focus on input factors and ‘visible productivity’ (time spent, sales calls made, lines of code written1, bugs closed) is a result of the availability heuristic and substitution bias in action.
The outcomes of an individual/team’s work are delayed and often diffused – hard to credit exactly. However, the inputs are visible and trivially measurable. In pursuit of productivity metrics, the manager/organisation substitute the hard to measure outcomes with the easily available input factors (time spent in office, calls made, lines of code) etc.
Continue reading Availability bias and the remote work advantage
Should we use multi-year, moving-average of income to calculate tax & benefit payments?
Income tax rates are based on current/last year’s income. This makes them easy to calculate and implement.
This immediacy of taxes also makes them painful, and makes the tax slab thresholds as artificial barriers to income mobility. An example of this is when we get a raise which pushes us from near the top end of one tax rate bracket, to the bottom end of a higher tax rate bracket. This frequently means that even though the employer is paying us more after the raise, we are actually taking home less money due to a higher tax rate.
Government benefits work similarly. For example, the unemployment benefit / social support payments cut off (or reduce dramatically) when we start working. However, after accounting for taxes and loss of benefits, the take home income from pay is often lower than the unemployment benefits.
Money is the final metric
This headline reminds me of the old principle that I emphasise:
Money is the final metric
If our metrics don’t directly correlate to, or convert into money1 in the near term, then they are not the correct metrics.
Too many metrics, in my experience, are designed for being:
- easy to measure (or easily available),
- easy to improve, and
- comfortable to explain
What they are not designed for: being strongly correlated with current or future supply of money.
For growth, revenue (total, unit, net unit) is the best metric.
Views of our videos2 can be good metrics if they convert directly into money:
– product sales show a direct correlation, or
– advertisers accept them as proxy for ad views, or
– investors require them as a valuation input for the next raise
Views of our videos are a bad metric when they aren’t directly impacting revenue. If sales aren’t growing in proportion with the views, then counting views is of no relevance to the health of the business.
The availability bias trip-wire
The availability heuristic operates on the notion that if something can be recalled, it must be important, or at least more important than alternative solutions which are not as readily recalled.
Most online advertising platforms understand this well, and use it to hook their customers (advertisers). If they show us good3, clean and easily accessible metrics of their choice, we will give those metrics more weight than they deserve.
The views count is right there – in the analytics dashboard. While finding the correct metric that actually correlates with sales, and then tracking it, can be hard.
This also ties in the second part of the hook. The view count number is also easily movable. Spend some money on advertisements, the view count will go up. Voila! View counts – a metric that is easy to measure, and easy to improve!
Those money-correlated metrics, they are even harder to improve than they are to discover and track. Oh look, the views went up again!
We all have heard of ‘Our viewer/user/visitor numbers were amazing, but the money ran out before…’
This is why the money ran out. Because we chose the easy metric, over the metric that really matters – money.
TIL: Moving average with missing periods in Google Sheets
The short answer:
=AVERAGEIFS(A1:A99, B1:B99,"<="&B2, B1:B99, ">"&B2 - 30)
Values to be averaged are in A1:A99, corresponding dates are in B1:B99.
What the formula does: average the values in the range – Include a value in calculating average for the current date if:
- The date for that value is same as or before the date in the current row, and
- The date for that value is greator than the date X days before the date in the current row (X is 30 in the formula for a 30-day moving average)
The long one:
I have a spreadsheet with my daily weight log. It has occasional missing days – when I didn’t log my weight.
Yesterday, I wanted to chart this data, and wanted to add a moving average to it. Google sheets’ in-built moving average trend line refused to work – either due to the missing data, or due to the number of entries. So I added a column to the sheet with the calculated (trailing) moving average weight.
I’ve never before had to calculate moving average over a non-consecutive data set. So, in case I forget, I’m noting it down here for later…
Continue reading TIL: Moving average with missing periods in Google Sheets