I Pre-Registered a Hypothesis. 600 API Calls Later, the Data Killed It.
📰 Dev.to · YuhaoLin2005
Learn how to design and run an experiment to test a hypothesis using API calls and analyze the results to validate or invalidate the hypothesis
Action Steps
- Design an experiment to test a hypothesis using API calls
- Run the experiment with a sufficient sample size (e.g. n=600)
- Collect and analyze the data from the API calls
- Compare the results to the initial hypothesis and determine whether it is validated or invalidated
- Refine the hypothesis and repeat the experiment if necessary
Who Needs to Know This
Data scientists, software engineers, and product managers can benefit from this lesson to improve their experimentation and hypothesis testing skills
Key Insight
💡 A well-designed experiment with sufficient data can invalidate even the most promising hypotheses
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📊 Ran an experiment with 600 API calls and the data killed my hypothesis! 💀
Key Takeaways
Learn how to design and run an experiment to test a hypothesis using API calls and analyze the results to validate or invalidate the hypothesis
Full Article
A stranger on DEV.to said "run this experiment." I ran it at n=600. Here's what happened — including...
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