Meso-problem Solving using Meso-patterns Meso-problems are software design and development problems that require short hard-limited time to solve satisfactorily with good quality. The time limit is usually not more than an hour. The prefix meso- means intermediate. These meso-problems are distinct from normal software design problems macro-problems which require much more time to solve and implementation idioms micro-problems that are usually implementation language-specific.
Alex Birkett Last updated: July 30, Reality check: Statistics provide inference on your results, and they help you make practical business decisions. Lack of understanding of statistics can lead to errors and unreliable outcomes.
First, I would measure the temperature of each coffee. Next, I would see which one has the highest temp. Finally, I would subtract the lower temp coffee from the higher to get the difference in temperature.
Obvious and super simple.
Any time we have to measure and compare collections of things, we need to use statistics. Conversion optimization is also a balancing act between exploration and exploitation.
As Ton Wesseling from Testing. Do you want to know the real answer, or do you just want to make a profit? If you are testing medicines that should stop someone from dying — and the A variant is not getting those medicines, when do you stop feeding Variant A with nothing when you see Variation B is working?
After a couple of positive tests? How many people need to die before you decide to give everyone Variation B. Mean Sampling Mean The mean is the average. In our coffee example, this would be the process of measuring the temperature of each cup of coffee that we sample and dividing by the total number of cups, reaching an average temperate that is hopefully representative of the actual average.
Variance Variance is the average variability of our data.
The higher the variability, the less precise the mean will be as a predictor of any individual data point. Many things can cause variance e. In terms of conversion optimization, Marketing Experiments gave a great example of variance in this blog post: It splits traffic between two identical pages, and if you discover that there is a statistically significant lift on one of the variations, you need to investigate the cause.
Therefore, we need to collect data on the temperature in order to estimate the average temperature. The more cups we measure, the more likely it is that the sample is representative of the actual temperature.
Similarly, in conversion optimization, the larger the sample size, in general, the more accurate your test will be.
Evan Miller wrote a well-known blog post on the topicwhich you should definitely read. Assuming there is no underlying difference between A and B, how often will we see a difference like we do in the data just by chance? Insignificant results carry a larger risk of false positives type I errors.
The test can and will oscillate between significant and insignificant at many points throughout the experiment: The biggest mistake beginning optimizers make is calling their tests early. Two days after the test started, here were the results: Variation clearly lost, right?This is the first tutorial in the "Livermore Computing Getting Started" workshop.
It is intended to provide only a very quick overview of the extensive and broad topic of Parallel Computing, as a lead-in for the tutorials that follow it. The level of statistical literacy is pretty poor in CRO world. Here's primer on A/B testing statistics to make sure you're not messing up tests.
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