How important is experimental design for data science?

 

What is Experimentation and why is it so important?

Experiment design is all about making sure that you have the necessary data to answer your data science questions efficiently.

Some issues may develop before you begin working on your data science troubles.

 

●      Is there a better method to respond to the query?

●      Is it clear what you're measuring, and how?

●      In order to make an educated decision, what information is required?

●      What's the best way to get my hands on that information?

●      What software and libraries do I use? etc…

All of these questions must be addressed at the beginning of a data science experiment. You'll encounter a number of these challenges if you don't prepare ahead and build your workflow properly.

Experimental Design Flow

1. Come up with a query

2. Experimentation with design

3. Identify issues and potential causes of inaccuracy

4. Gather information

Before any data collection begins, you must clearly define your questions, then build the best possible set-up to gather the data to answer your questions, and only then, collect data.

Importance

●      Wrong conclusions result from bad data and incorrect analysis.

●      People can be swayed by erroneous conclusions that have a ripple effect (citations in papers eventually is applied in real medicinal cases)

●      Experiment design is critical for investigations with high stakes, such as determining cancer patients' treatment programmes.

●      They will be retracted and have a terrible reputation for papers that are based on bad data and incorrect conclusions.

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