AI TRAINING
Statistical Foundations for Business Analytics
Build the statistical intuition to design experiments, interpret results, and avoid common analytical traps.
What it covers
This programme covers the core statistical concepts every analyst needs: descriptive statistics, probability distributions, hypothesis testing, confidence intervals, and A/B test design. Participants work through realistic business datasets to move from data summaries to defensible conclusions. The format combines short concept modules with hands-on lab exercises using Excel, Python, or R depending on team preference. By the end, participants can independently design and interpret experiments and communicate uncertainty to non-technical stakeholders.
What you'll be able to do
- Select the appropriate descriptive statistic for a given business question and explain why
- Design a valid A/B test including sample size calculation, randomisation, and stopping criteria
- Correctly interpret a p-value and confidence interval without overstating certainty
- Identify at least three common statistical pitfalls (p-hacking, Simpson's paradox, survivorship bias) in a real dataset
- Present statistical findings — including uncertainty — in a format accessible to non-technical decision-makers
Topics covered
- Descriptive statistics: mean, median, variance, skewness, and when each matters
- Probability distributions: normal, binomial, Poisson — recognising them in business data
- Hypothesis testing: null vs. alternative, p-values, Type I and Type II errors
- Confidence intervals and margin of error in plain language
- A/B test design: sample size, power, and stopping rules
- Correlation vs. causation and Simpson's paradox
- Common statistical pitfalls: p-hacking, survivorship bias, base rate neglect
- Communicating statistical findings to non-technical audiences
Delivery
Typically delivered as a 2–3 day in-person or live-virtual programme split across multiple sessions to allow reflection between modules. Approximately 40% concept delivery and 60% hands-on lab work using real or realistic business datasets. Materials include annotated slide decks, lab notebooks (Excel or Jupyter), a cheat-sheet reference card, and a take-home case study. Remote delivery works well with breakout rooms for group exercises; in-person preferred for cohort bonding and live dataset exploration.
What makes it work
- Anchoring every statistical concept to a real business decision the team already faces
- Requiring participants to bring one live dataset from their own work to the lab sessions
- Establishing a shared review checklist for experiment design that the team uses after the training
- Following up 4–6 weeks post-training with a short office-hours session to review live experiments
Common mistakes
- Running A/B tests without pre-calculating required sample size, leading to underpowered or over-run experiments
- Treating p < 0.05 as proof of business impact rather than as a signal to investigate further
- Confusing correlation with causation when presenting dashboard insights to leadership
- Stopping tests early when results look promising, inflating false-positive rates
When NOT to take this
This training is not the right fit for a team that already runs hundreds of experiments per month with a dedicated data science function — they need advanced causal inference or Bayesian methods training, not statistical foundations.
Providers to consider
Sources
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