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Describe data cleaning and data dredging1/21/2024 ![]() using appropriate statistical methods and a large enough sample size - this type of data exploration can be useful for generating hypotheses or reporting on the “state of affairs” in the domain of your data. While the phrase often has a negative connotation, when done correctly - i.e. In many data and science spaces, this type of headfirst-diving into analysis without a focused hypothesis driving the exploration is referred to as a “fishing expedition” or “ data dredging." That is, analyzing random questions as they popped into my head, always on the hunt for a result that was interesting. I would eventually find a dataset that piqued my interest and start “poking” at it. ![]() Not only were there seemingly limitless datasets to choose from, the datasets themselves were often large and unexplored. When I was first learning how to work with open data, I would often get lost in the possibility of it all. But given the sheer volume of data available, where do you start? ![]() Whatever your focus, with so much data at your fingertips, it can be tempting to utilize these resources to improve your data processing skills, uncover new information, join a data competition, and solve real world problems. There are literally millions of datasets on the internet that are open and freely available to use. ![]()
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