But this can result in an analytical process that is overly specific to the initial dataset, making it difficult to repeat or apply to updated data with slight differences. I still came up with an interesting insight.
Errors of Applicability. Thinking about how to write results before solidifying the research questions ensures the analysis is able to answer the questions. Richer metadata, including formatting and units would allow tools to apply dimensional analysis ideas to prevent silly mistakes and present output in forms less prone to misinterpretation.
It seems likely though not certain that a richer type system could allow us to capture the otherwise implicit assumptions we make as we perform data transformations. But a logistic regression can also incorporate covariates, directly test interactions, and calculate predicted probabilities.
He has also, while at Quadstone, combined stochastic optimization with data mining to allow new classes of problems to be tackled.An ad hoc approach is common during initial data exploration. Nicholas Radcliffe, Founder, Stochastic Solutions If, as Niels Bohr maintained, an expert is a person who has made all the mistakes that can be made in a narrow field, we consider ourselves expert data scientists. Data transformations often have unpredictable consequences in the face of unexpected data missing or duplicate values being a common problem and can lead to unjustifiable results. One of the founding visions of Stochastic Solutions is to help companies improve their approach to the systematic design and measurement of direct marketing activities in ways that bring immediate benefits while also preparing them to be able to evaluate properly the potentially huge benefits of adopting this radical new approach. Tests can prove that input data matches our expectations, and that our analysis can be replicated independently of hardware, parallelism, and external state such as passing time and random seeds. While there, he led the development of a radically new algorithmic approach to targeting direct marketing which has repeatedly proved capable of delivering dramatic improvements to the profitability of both traditional outbound and more modern inbound marketing approaches, in an approach known as uplift modelling. Data analysis also offers a plethora of new ways to fail. The most basic kind of error is where we just get the program wrong—either in obvious ways like multiplying instead of dividing—or in subtler ways like failing to control an accumulation of numerical errors e. A chi-square test can do none of these.
It pretty much comes down to two things: whether the assumptions of the statistical method are being met and whether the analysis answers the research question. Applying statistical methods or inferences correctly often require that specific assumptions be satisfied.
Several years ago, as we began to realize the benefits of Test Driven Development in our traditional software development, we asked ourselves whether a similar methodology could inform and improve our approach to data analysis.
These specification errors are often not discovered until much later, if at all.
We believe that the principles of test-driven development provide a promising approach to catching and preventing many of these kinds of errors much earlier.