%% date:: [[2022-09-29]] %% # [[Principles for analyzing results]] Below are some general guiding principles for analyzing any data. - [[Compare experimental results to live results]] - [[Increase granularity to spot patterns]] - Look for relationships between variables - Use [[Raw data]] where possible - Disaggregate - [[Disaggregate by origin]] - [[Disaggregate by characteristics]] - [[Transform data through visualization]] - "The way data is communicated in a chart can easily mislead others, so we need to make sure that the message we send reflects the facts. Decisions that we make that we think are just cosmetic can cause our data to be misinterpreted by people who aren't statisticians." - ""`A table can be considered as a type of graphic, and requires careful design choices of colour, font and language to ensure engagement and readability. The audience’s emotional response to the table may also be influenced by the choice of which columns to display.` ([Location 432](https://readwise.io/to_kindle?action=open&asin=B07N6D73FZ&location=432))"" - ""`Table 1.1 shows the results in terms of both survivors and deaths, but in the US mortality rates from child heart surgery are reported, while the UK provides survival rates. This is known as negative or positive framing, and its overall effect on how we feel is intuitive and well-documented: ‘5% mortality’ sounds worse than ‘95% survival’. Reporting the actual number of deaths as well as the percentage can also increase the impression of risk, as this total might then be imagined as a crowd of real people. `([Location 434](https://readwise.io/to_kindle?action=open&asin=B07N6D73FZ&location=434))"" - ""`Note the two tricks used to manipulate the impact of this statistic: convert from a positive to a negative frame, and then turn a percentage into actual numbers of people. Ideally both positive and negative frames should be presented if we want to provide impartial information, although the order of columns might still influence how the table is interpreted.` ([Location 447](https://readwise.io/to_kindle?action=open&asin=B07N6D73FZ&location=447))"" - ""`Choosing the start of the axis therefore presents a dilemma. Alberto Cairo, author of influential books on data visualization,3 suggests you should always begin with a ‘logical and meaningful baseline’,` ([Location 460](https://readwise.io/to_kindle?action=open&asin=B07N6D73FZ&location=460))"" - "categorizing by transaction, load generator region, and systems involved" - [[Analyze results for steady state only]] - When examining response times, exclude errors - [[Reduce the influence of an outlier]] ## References - [[Analyzing load testing results]]