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se:labs:07 [2024/11/26 16:09] cosmin.dumitrache [2. Experiments] |
se:labs:07 [2024/11/28 16:35] (current) cosmin.dumitrache |
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| ==== Introduction ==== | ==== Introduction ==== | ||
| - | When you're working on your product and launching new features you will often find yourself in a situation where you are not sure if launching a new feature is good or bad. This is where experimentation tools come in. Such tools allow us to run A/B tests in order to make decision in a more rigorous scientific way. A/B tests as the name suggest allow us to compare two (or more) versions of a feature in a statistically significant way. | + | When you're working on your product and launching new features you will often find yourself in a situation where you are not sure if launching a new feature is good or bad. This is where experimentation tools come in. Such tools allow us to run A/B tests in order to make decisions in a more rigorous scientific way. A/B tests as the name suggest allow us to compare two (or more) versions of a feature in a statistically significant way. |
| In this lab we are going to be using [[https://posthog.com/|PostHog]] and two key tools to help us run our experiment: | In this lab we are going to be using [[https://posthog.com/|PostHog]] and two key tools to help us run our experiment: | ||
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| For an early-stage product you're probably going to want to be focusing more on types of experiments with high potential impact. This is because: | For an early-stage product you're probably going to want to be focusing more on types of experiments with high potential impact. This is because: | ||
| - As an early-stage startup you probably will not have a big enough volume of users to get statistically significant data in a reasonable amount of time unless the experiment makes a big impact (in which case you will be able to make decisions with less data). | - As an early-stage startup you probably will not have a big enough volume of users to get statistically significant data in a reasonable amount of time unless the experiment makes a big impact (in which case you will be able to make decisions with less data). | ||
| - | - Your product is not mature so it makes more sense to work on things with high impact first. Making improvements with low impact is not the optimal path to finding product/market fit. So best to defer these for later. | + | - Your product is not mature so it makes more sense to work on things with high impact first. Making improvements with low impact is not the optimal path to finding product/market fit. So it's best to defer these for later. |
| </note> | </note> | ||
