Catalog
concept#Analytics#Data#Software Engineering

Inferential Statistics

Methods for drawing conclusions about populations from sample data; includes estimation, hypothesis testing and confidence intervals.

Inferential statistics provides methods to draw conclusions about populations from sample data using probability models.
Established
High

Classification

  • Medium
  • Technical
  • Design
  • Intermediate

Technical context

Statistical programming languages (R, Python/statsmodels)Data platforms and ETL pipelinesReporting and dashboarding tools for results

Principles & goals

Explicit formulation of hypotheses and assumptionsQuantify uncertainty instead of binary statementsValidate model assumptions and perform sensitivity checks
Discovery
Domain, Team

Use cases & scenarios

Compromises

  • Incorrect conclusions if assumptions are violated
  • Misinterpretation of p-values
  • Overreliance on statistical significance without practical relevance
  • Document hypotheses and analysis plans before registration
  • Conduct robustness and sensitivity analyses
  • Communicate results with uncertainty measures and limitations

I/O & resources

  • Sample data with measurements
  • Definition of hypotheses or parameters to estimate
  • Assumptions about distributions or models
  • Estimates with uncertainty quantification
  • Hypothesis test results and decision basis
  • Recommendations for further data collection or modeling

Description

Inferential statistics provides methods to draw conclusions about populations from sample data using probability models. It covers estimation, hypothesis testing, confidence intervals and model-based inference. Practitioners use these techniques to quantify uncertainty, test hypotheses, and support data-driven decisions across scientific and business domains.

✔

  • Enables generalizable conclusions from samples
  • Provides quantified measures of uncertainty
  • Supports data-driven decision making

✖

  • Dependence on model and distributional assumptions
  • Sensitivity to bias in sampling
  • Not always directly transferable with small samples

  • Confidence interval width

    Measures precision of an estimate; narrower intervals indicate higher precision.

  • Power / statistical power

    Probability of detecting a true effect; depends on sample size and effect size.

  • Type I/II error rates

    Frequency of erroneous decisions (false rejection/acceptance of the null hypothesis).

Confidence interval for mean salary

Calculation of a 95% confidence interval from an employee sample to estimate average salary.

Linear regression for effect size estimation

Estimating the impact of a training intervention on sales including confidence intervals and p-values.

A/B test evaluating a new feature variation

Analysis of an A/B test with hypothesis testing and reporting of statistical significance and effect size.

1

Define research question and hypotheses, clarify data requirements.

2

Collect data, clean and perform exploratory analysis.

3

Select appropriate statistical methods, check assumptions and report results.

⚠️ Technical debt & bottlenecks

  • Unstructured raw data without metadata hinders replication
  • Outdated analysis scripts without tests and documentation
  • Missing pipeline for reproducible statistical analyses
Insufficient sample sizesBiased or non-representative dataLack of statistical expertise in the team
  • Making decisions despite unreliable p-values from a small sample
  • Neglecting measurement error in data when estimating
  • Reporting statistical significances without context or effect sizes
  • Automatically applying complex models without checking assumptions
  • Confusing correlation with causation
  • Insufficient consideration of multiple testing issues
Foundations of probability and statisticsSkills in data cleaning and preprocessingExperience with statistical software and model validation
Data quality and representativenessAvailability of sufficient sample sizesTransparent assumptions and traceability
  • • Representative samples may be hard to obtain
  • • Legal or privacy-related restrictions
  • • Time and budget constraints for data collection