analysis

Something very useful in a disorder is: it’s only a disorder if you can’t manage it. — Kindle Customer in comment to Backfired: Attention Deficit Audiobook

What is this? | Xmrit Xmrit (pronounced ‘xam-mer-it’) is a free tool by Commoncog to help business operators quickly create and share XmR charts. An XmR chart helps you analyse your data. In a sentence: it helps you separate signal from noise. It is particularly useful to business operators. … XmR charts are so named because they consist of an ‘X’ chart (the ‘X’ variable, or the metric you care about), and a ‘Moving Range’ chart, which shows differences from point to point. ...

The way to mitigate these unintended effects is to replace Personas with models that enable cohesive stories. These models are called Characters . What would make sense for the brain is a believable story which explains that purchase. This is what we can use Characters for. A Character is someone who: Has anxieties & motivations. Experiences purchase-progress events. Encounters purchase-progress situations. (via Replacing Personas With Characters — Medium )

Replacing The User Story With The Job Story We frame every design problem in a Job, focusing on the triggering event or situation, the motivation and goal, and the intended outcome: When _____ , I want to _____ , so I can _____ . For example: When an important new customer signs up, I want to be notified, so I can start a conversation with them. See also 5 Tips For Writing A Job Story: ...

When optimal matters | Playing with Objects The rules of optimization (…) When you are thinking on making an optimization: First time: Don’t do it! Second time: Don’t do it yet! Third time: Ok, but you first profile and measure, and then optimize

73.6% of all Statistics are Made Up — How to Interpret Analysts Reports The problem of skewing results: (…) How is it skewed? There are so many ways to present data to tell the story you want that I can’t even list every way data is skewed. Here are some examples: You ask a small sample set so that data isn’t statistically significant . This is often naiveté rather than malicious You ask a group that is not unbiased. (…) This type of statistical error is known as " selective bias ." Also common, you look at a large data set of questions asked about consumer preferences. You pick out the answers that support your findings and leave out the ones that don’t support it from your report. This is an " error of omission ." You change the specific words asked in the survey such that you subtly change the meaning for the person reading your conclusions. But subtle changes in words can totally change the way that the reader interprets the results. Also common is that the survey itself asks questions in a way that leads the responder to a specific answer. There are malicious data such as on Yelp where you might have a competitor that types in bad results on your survey to bring you down (…) (…) As my MBA statistics professor used to say, “seek disconfirming evidence.” That always stuck with me. ...

The Affinity Diagram (KJ Analysis) Affinity diagram – Wikipedia : The tool is commonly used within project management and allows large numbers of ideas stemming from brainstorming to be sorted into groups, based on their natural relationships, for review and analysis.