My research showed that organizing the data into

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For example: “The results are statistically significant with 95% confidence” indicates that the alpha was 0.05, meaning there's a 1 in 20 chance of error in the results.

a simple chart for clarity is the best way to start.

Since I’m testing two variations (Subject Line A and Subject Line B) and two outcomes (opened, did not open), I can use a 2x2 chart:

Outcome

Subject Line A

Subject Line B

Total

Opened

X (e.g., 125)

Y (e.g., 135)

X + Y

Did Not Open

With (eg, 375)

W (eg, 365)

From + In

Total

X + Z

Y + W

N

This makes it easy to visualize the data and georgia phone number material calculate your Chi-Squared results. Totals for each column and row provide a clear overview of the outcomes in aggregate, setting you up for the next step: running the actual test.

While tools like HubSpot's A/B Testing Kit can calculate statistical significance automatically, understanding the underlying process helps you make better testing decisions. Let's look at how these calculations actually work: