l stoodialinza

STOODIA LINZA

Statistics: concepts and methods

Each topic includes a concise account of the method, with formulas and assumptions, a plain-language explanation, and examples of how to use it in Linza. It also covers common mistakes and offers an optional self-check question. Choose the level of detail you need.

PracticeThree kinds of tasks: hand calculations with new numbers, method selection from a study design, and interpreting Linza results calculated from synthetic data. Answers are not saved.Glossary of termsBrief explanations of terms and symbols used in results. Each entry links to a topic with a fuller explanation.Sample size and power planningEnter an effect, α and target power. The calculator estimates sample size for a two-sided t-test with independent groups or paired measurements, and for a one-way ANOVA with equal group sizes. Calculations have been checked against SciPy.
Step 1

Basics

Data, typical values and spread, samples. No background needed.

  1. 01Data: observations and variablesBefore calculating anything, decide what one observation in the table is and what type each quantity has.5 min
  2. 02Typical value and spreadMean, median and standard deviation: how to describe a group with one or two numbers, and when those numbers mislead.6 min
  3. 03Samples, SEM and confidence intervalsA sample can estimate the mean of a defined population. How precise is that estimate?7 min
Step 2

Drawing conclusions

Hypothesis tests, p values, effect sizes, assumptions and multiple comparisons.

  1. 04Hypotheses, p-values and αWhat statistically significant really means, and what a p-value does not tell you.8 min
  2. 05Effect size and powerWhether a difference is significant and how large it is are two different questions.7 min
  3. 06Test assumptionsAssumptions describe the study design and data model. Diagnostics can reveal some problems, but cannot confirm every condition.7 min
  4. 07Multiple comparisonsMany tests increase the chance of at least one false finding. Choose a correction for a family of hypotheses defined in advance.7 min
Step 3

Methods

Specific tests and models available in Linza.

  1. 08Comparing two groupsFor two groups, choose a method from the data structure: independent observations or matched pairs. Each design has parametric and rank-based methods.8 min
  2. 09Several groups: one-way ANOVA and alternativesHow to compare three or more independent groups while accounting for multiple comparisons.8 min
  3. 10Regression and correlationHow Y depends on X, and how closely the points follow the line.8 min
  4. 11Dose-response curves: the four-parameter logistic modelS-shaped curves, EC50 and IC50, dose-range planning and curve comparisons.11 min
  5. 12Categorical data: contingency tablesHow to compare proportions and categories: χ², Fisher’s exact test, odds ratios and paired data.8 min
  6. 13Time to event: Kaplan-Meier and log-rankHow to use observations in which the event has not happened yet.11 min
Step 4

Complex designs

Factors, repeated measures, mixed models and reproducibility.

  1. 14Two factors and repeated measuresMain effects and factor interactions, plus repeated measurements on the same experimental units.10 min
  2. 15ANCOVA: comparing groups after covariate adjustmentAdjust group means for a continuous covariate, then assess the common-slope assumption.10 min
  3. 16Mixed model with a random interceptHow mixed models analyze incomplete repeated measurements, and which assumptions to justify.10 min
  4. 17Data preparation and a reproducible reportTransformations, normalization, exclusions and what belongs in the Methods section.7 min