Lära Variance Monitoring

Variance monitoring is crucial for running robust experiments, including A/B tests. You must pay attention not only to the average of your key metrics, but also to how much those metrics fluctuate over time.

What is Variance?

Variance measures how much individual data points differ from the mean;
High variance means your data points are spread out widely;
Low variance means your data points are tightly clustered around the mean.

Why Monitor Variance?

High or unstable variance can signal:
- Data quality problems;
- Process changes;
- Technical errors.
These issues can undermine the validity of your experiment.

By actively monitoring variance in your key metrics, you can:

Quickly identify unusual behavior in your data;
Protect your experiment from misleading results;
Maintain trust in your findings.


              123456789101112131415161718192021222324252627
            
import pandas as pd
import matplotlib.pyplot as plt

# Simulated daily experiment metric data
data = {
    "date": pd.date_range(start="2024-01-01", periods=30, freq="D"),
    "metric_value": [
        100, 102, 98, 97, 101, 99, 100, 98, 97, 105,
        150, 152, 148, 151, 149, 150, 151, 149, 148, 152,
        100, 101, 99, 98, 102, 100, 99, 101, 98, 100
    ]
}
df = pd.DataFrame(data)

# Calculate rolling variance (window of 7 days)
df["rolling_variance"] = df["metric_value"].rolling(window=7).var()

# Plot variance over time
plt.figure(figsize=(10, 5))
plt.plot(df["date"], df["rolling_variance"], marker="o", label="7-day Rolling Variance")
plt.axhline(y=300, color="red", linestyle="--", label="Variance Threshold")
plt.xlabel("Date")
plt.ylabel("Variance")
plt.title("Variance Monitoring of Key Metric Over Time")
plt.legend()
plt.tight_layout()
plt.show()

When variance in your key metrics exceeds acceptable thresholds, you need to respond quickly to protect your experiment. Follow these steps:

Pause the experiment; do not continue collecting data until you understand the issue;
Investigate potential causes, such as:
- Data pipeline disruptions;
- Changes in user behavior;
- Technical glitches.
Check for outliers or data entry errors that may be inflating the variance;
Address the root cause if you find one, and consider excluding affected data points from your analysis;
If the cause is unclear or cannot be resolved, pause or restart the experiment to maintain data integrity.

Always document any variance issues and your response. This transparency is essential for interpreting experimental results and planning future experiments.

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 6. Kapitel 2

Fråga AI

Fråga vad du vill eller prova någon av de föreslagna frågorna för att starta vårt samtal

Suggested prompts:

Can you explain how to interpret the rolling variance plot?

What should I do if I notice a sudden spike in variance?

How do I set an appropriate variance threshold for my experiment?

Awesome!

Completion rate improved to 3.23

Svep för att visa menyn