Data Science

Statistical Inference

We can draw formal conclusions about a population from noisy statistical data. Our statistical protocols can successfully quantify noise in your data and make reliable inferences or decisions about general population. In statistical inference we specifically focus on following aspects;

  • Does sample data truly represent the population?
  • What possible assumptions and thoughts can be considered about population?
  • How can we model underlying phenomena?
  • Is there any missing data and how to quantify resultant bias?
  • How to adjust for random effect, if there is any?

In statistical inference, the methods we apply include, but not limited, to the following;

  • Descriptive statistics
  • Statistical models – Bernoulli, normal, Poisson etc.
  • Conditional probabilities and Bayes’ theorem
  • Central limit theorem
  • Hypothesis testing


R, SAS, SPSS, Matlab