{"id":6068,"date":"2016-03-06T20:38:30","date_gmt":"2016-03-06T15:08:30","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=6068"},"modified":"2016-09-11T10:59:27","modified_gmt":"2016-09-11T05:29:27","slug":"right-sample-size-analysis","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/right-sample-size-analysis\/","title":{"rendered":"What is the Right Sample Size for Your Analysis?"},"content":{"rendered":"
\"Sample<\/a>

Sample Size & Simulation – by Roopam<\/p><\/div>\n

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A few years ago, I did a day-long workshop on Statistical Thinking<\/em>\u00a0for a large German shipping & cargo company in Mumbai. During the Q&A session, the Vice President of operations asked a tricky question: what is a good sample size to achieve precision and accuracy for your analysis?\u00a0He was looking for a one-size-fits-all answer and I wish it were that simple.<\/p>\n

On the surface, this might seem to be an extremely fundamental and basic question but in practice, so many professionals struggle to answer this. In this article, I\u00a0will try to answer this question.<\/p>\n

Sample Size<\/span><\/h2>\n

Like ingredients are at the core of taste of the food, sample size and data quality are at the core of the analysis. With bad ingredients, even a master chef can’t produce a good dish. Hence, representative sample\u00a0and sample size are the two most important aspects on which every data scientist needs to keep a\u00a0cautious eye.<\/p>\n

One of the most intuitive ways to identify the right sample size is through data simulation. In this article, we will simulate coin tosses to understand the significance of sample size. As you know tossing a coin is one of the easiest and fastest experiments with random binary outcomes.<\/p>\n

Before you brush off coin tosses as too elementary and textbook-ish please check out the\u00a0following practical events with random binary outcomes similar to coin tosses.<\/p>\n