從基礎概念到進階分析——本教程涵蓋描述統計、機率分布、抽樣與中央極限定理、假設檢定、回歸、ANOVA、存活分析及樣本數設計，並配有互動模擬。
From foundational concepts to advanced analyses — this tutorial covers descriptive statistics, probability distributions, sampling and the central limit theorem, hypothesis testing, regression, ANOVA, survival analysis and study design, with interactive simulations.
探索平均數、中位數、變異數、標準差與箱型圖，瞭解如何總結數據。
Explore mean, median, variance, standard deviation and box plots to summarise data.
認識常見的常態與二項分布，調整參數並觀察型態變化。
Learn about normal and binomial distributions, adjust parameters and see how the shapes change.
模擬抽樣分佈，體驗隨樣本數增加而趨近常態的過程。
Simulate sampling distributions and experience how they approach normality as sample size grows.
瞭解零假設、對立假設、p 值、型一與型二錯誤的意義。
Understand null and alternative hypotheses, p‑values and type I/II errors.
比較平均值並建立信賴區間，學習如何做出推論。
Compare means and build confidence intervals to make inferences.
檢驗類別變項之間的關聯，判斷兩變量是否獨立。
Test associations between categorical variables and determine independence.
比較三組以上的平均值，了解 F 統計量與事後檢定。
Compare means of three or more groups using the F statistic and post‑hoc tests.
建立最佳擬合線，解釋截距、斜率與決定係數。
Fit the best line and interpret the intercept, slope and R².
預測二元或多元類別結果，理解 S 型連續機率模型。
Predict binary or multi‑category outcomes with S‑shaped probability models.
了解時間到事件資料、Kaplan‑Meier 曲線與截尾觀察。
Learn time‑to‑event data, Kaplan–Meier curves and censoring.
評估型一、型二錯誤，計算所需樣本數以獲得足夠檢定力。
Balance type I/II errors and compute required sample sizes for adequate power.