Tag: casestudy

Solutions to working with small sample sizes

Solutions to working with small sample sizes

Both in science and business, we often experience difficulties collecting enough data to test our hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs.

Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the questions we’re really interested in.

Several scholars teamed up and wrote this open access book: Small Sample Size Solutions.

This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample studies. Each chapter illustrates statistical methods that allow researchers and analysts to apply the optimal statistical model for their research question when the sample is too small.

This book will enable anyone working with data to test their hypotheses even when the statistical model required for answering their questions are too complex for the sample sizes they can collect. The covered statistical models range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R.

You can access the book for free here!

AI at P&G and American Express

HBR frequently features articles that elaborate on how management approaches are changing as a response to the rise of analytics. Authors Thomas Davenport and Randy Bean notice that “there is a tendency with any new technology to believe that it requires new management approaches, new organizational structures, and entirely new personnel.” This is not true they claim, and they continue to provide “two good examples of combining well-established practices with cognitive technology to achieve business success: Procter &Gamble and American Express. These two companies employ several (seemingly) best practices that prove successful in the transition to the digital age:

  • Build on current strengths: Current analytical personnel can (easily) be trained to work with machine learning techniques. Cognitive technology and AI are not so much a new domain, as they are extensions of applied statistics.
  • Focus on talent: Build your data science talent pool by combining internal development and mobility with external hiring.
  • Do it yourself: It is often more effective and cost-efficient to develop analytical capabilities internally, than to partner up with consultants/vendors.
  • A customer focus: Focus on win-win applications first, those which create value for the organization as well as the customers.
  • Augmentation, not automation: Focus should not be on cutting labor costs (automating jobs), but on creating human-AI synergies (augmenting jobs).

Read the full article here.