statistical-analysis

SKILL.md 节选

# Statistical Analysis Skill

Descriptive statistics, trend analysis, outlier detection, hypothesis testing, and guidance on when to be cautious about statistical claims.

## Descriptive Statistics Methodology

### Central Tendency

Choose the right measure of center based on the data:

| Situation | Use | Why |
|---|---|---|
| Symmetric distribution, no outliers | Mean | Most efficient estimator |
| Skewed distribution | Median | Robust to outliers |
| Categorical or ordinal data | Mode | Only option for non-numeric |
| Highly skewed with outliers (e.g., revenue per user) | Median + mean | Report both; the gap shows skew |

**Always report mean and median together for business metrics.** If they diverge significantly, the data is skewed and the mean alone is misleading.

### Spread and Variability

- **Standard deviation**: How far values typically fall from the mean. Use with normally distributed data.
- **Interquartile range (IQR)**: Distance from p25 to p75. Robust to outliers. Use with skewed data.
- **Coefficient of variation (CV)**: StdDev / Mean. Use to compare variability across metrics with different scales.
- **Range**: Max minus min. Sensitive to outliers but gives a quick sense of data extent.

### Percentiles for Business Context

Report key percentiles to tell a richer story than mean alone:

```
p1:   Bottom 1% (floor / minimum typical value)
p5:   Low end of normal range
p25:  First quartile
p50:  Median (typical user)
p75:  Third quartile
p90:  Top 10% / power users
p95:  High end of normal range
p99:  Top 1% / extreme users
```

**Example narrative**: "The median session duration is 4.2 minutes, but the top 10% of users spend over 22 minutes per session, pulling the mean up to 7.8 minutes."

### Describing Distributions

Characterize every numeric distribution you analyze:

- **Shape**: Normal, right-skewed, left-skewed, bimodal, uniform, heavy-tailed
- **Center**: Mean and median (and the gap between them)
- **Spread**: Standard deviation or IQR
- **Outliers**: How many and how extreme
- **Bounds**: Is there a natural floor (zero) or ceiling (100%)?

## Trend Analysis and Forecasting

### Identifying Trends

**Moving averages** to smooth noise:
```python
# 7-day moving average (good for daily data with weekly seasonality)
df['ma_7d'] = df['metric'].rolling(window=7, min_periods=1).mean()

# 28-day moving average (smooths weekly AND monthly patterns)
df['ma_28d'] = df['metric'].rolling(window=28, min_periods=1).mean()
```

**Period-over-period comparison**:
- Week-over-week (WoW): Compare to same day last week
- Month-over-month (MoM): Compare to same month prior
- Year-over-year (YoY): Gold standard for seasonal businesses
- Same-day-last-year: Compare specific calendar day

**Growth rates**:
```
Simple growth: (current - previous) / previous
CAGR: (ending / beginning) ^ (1 / years) - 1
Log growth: ln(current / previous)  -- better for volatile series
```

### Seasonality Detection

Check for periodic patterns:
1. Plot the raw time series -- visual inspection first
2. Compute day-of-week averages: is there a clear weekly pattern?
3. Compute month-of-year averages: is there an annual cycle?
4. When comparing periods, always use YoY or same-period comparisons to avoid conflating trend with seasonality

### Forecasting (Simple Methods)

For business analysts (not data scientists), use straightforward methods:

- **Naive forecast**: Tomorrow = today. Use as a baseline.
- **Seasonal naive**: Tomorrow = same day last week/year.
- **Linear trend**: Fit a line to historical data. Only for clearly linear trends.
- **Moving average forecast**: Use trailing average as the forecast.

**Always communicate uncertainty**. Provide a range, not a point estimate:
- "We expect 10K-12K signups next month based on the 3-month trend"
- NOT "We will get exactly 11,234 signups next month"

**When to escalate to a data scientist**: Non-linear trends, multiple seasonalities, external factors (marketing spend, holidays), or when forecast accuracy matters for resource allocation.

## Outlier and Anomaly Detection

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