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#+title: ise_week_7
#+filetags: :uni:notes:

* DONE Statistical Test Selection and Use (pages 21-67)

* 7.1 Comparing Algorithms in Intelligent Software Engineering

** 1. Motivation

- Algorithms and configurations vary in performance.
- No universally best algorithm: performance depends on the specific problem ("No Free Lunch" theorem).
- To determine which algorithm/configuration is suitable, **comparison is essential**.
- However, comparison is challenging due to the **stochastic nature** of computational intelligence algorithms.

** 2. Stochastic Behaviour in Algorithms

- Sources of randomness:
  - In the algorithm (e.g., random initial population, stochastic gradient descent, mutation/crossover probabilities).
  - In data sampling.
- Result: Running the same algorithm multiple times on the same problem yields **different results**.
- Therefore, comparisons must account for this randomness.

** 3. Handling Stochastic Behaviour

- To compare algorithms meaningfully:
  - Run each algorithm **multiple times** (e.g., 30+ runs) using **different random seeds**.
  - This helps capture typical performance and reduce reliance on single-run outliers.

** 4. Methods for Comparison

*** a. Mean (Average)

- Simple and common.
- Problems:
  - Sensitive to **outliers**.
  - Does not represent **variability** in results.

*** b. Mean + Standard Deviation

- Adds information about variation.
- Still affected by outliers.
- Hard to tell whether differences are statistically significant.

*** c. Median

- More robust to outliers.
- Example:
  - Sorted list: 0.000001, 0.6, 0.62, 0.65, 0.7, 0.75, 0.8, 0.8, 0.81
  - Median = 0.7
- Problem: Ignores **variation** in data.

*** d. Median + Quartiles

- 1st and 3rd quartiles provide information about data spread.
- Still doesn't guarantee ability to distinguish between groups.

*** e. Statistical Hypothesis Testing

- Scientific method to determine if observed differences are **statistically significant**.
- Necessary for robust and credible comparison of algorithms.

** 5. Statistical Hypothesis Testing: Process

1. Define what to compare (e.g., accuracy or fitness).
2. Ensure fair comparison:
   - Equal number of evaluations or explain why not.
   - Example: Adjust generations to equate computational budget across algorithms.
3. Formulate hypotheses:
   - **Null hypothesis (H₀)**: No difference between the two groups.
   - **Alternative hypothesis (H₁)**: A statistically significant difference exists.
4. Select an appropriate test based on data distribution.

** 6. Choosing the Test

*** a. Normality Assumption

- Many statistical tests assume a **normal distribution** of values.
- Visual inspection or tests (e.g., Shapiro-Wilk) can check this.

*** b. Parametric vs Non-parametric Tests

- Parametric tests (e.g., t-test):
  - More powerful.
  - Require assumptions (e.g., normality, homogeneity of variance).
- Non-parametric tests (e.g., Wilcoxon, Mann-Whitney):
  - Safer for non-normal data.
  - Widely used in stochastic algorithm comparisons.

*** c. Paired vs Unpaired Tests

- **Paired**: Use when comparing results from same initial conditions.
- **Unpaired**: Use when runs are completely independent.

** 7. Test Outputs

- Test produces a **statistic** and a **p-value**.
  - If **p ≤ 0.05**, reject H₀: significant difference exists.
  - If **p > 0.05**, do not reject H₀: no significant difference found.
- Significance level is usually set to **0.05**, corresponding to 95% confidence.
- Lower significance (e.g., 0.01) may be used in critical applications.

** 8. Interpreting P-Values

- High p-value → Observed difference likely due to chance → **Do not reject H₀**.
- Low p-value → Observed difference unlikely due to chance → **Reject H₀**.

** 9. Test Examples (in R)

- Two-tailed Wilcoxon Rank-Sum Test (unpaired).
- Two-tailed Wilcoxon Signed-Rank Test (paired).

** 10. Multiple Comparisons Problem

- Comparing many algorithms or configurations increases the risk of **Type I errors** (false positives).
- Correction methods:
  - Adjust the significance threshold (e.g., Bonferroni correction).
  - Downside: Conservative → **reduced power** (risk of missing real differences).

** 11. Tests for N Groups

- Stronger than multiple pairwise tests with correction.
- Common tests:
  - **Kruskal-Wallis Test**: for unpaired comparisons across groups.
  - **Friedman Test**: for paired comparisons across groups.

*** Post-hoc Analysis

- Needed when the global test finds significant differences but doesn't specify **which pairs** differ.
  - Kruskal-Wallis → Dunn post-hoc test.
  - Friedman → Nemenyi post-hoc test.

