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During World War II, analysts examined bombers returning from missions and considered adding armour where bullet holes were concentrated.

Statistician Abraham Wald spotted the flaw: they were only looking at planes that survived.

The areas with fewer bullet holes could actually be the most vulnerable, because planes hit there were less likely to return.

This is a classic example of survivorship bias.

The same lesson applies to reliability analysis. Looking only at products that failed can give you an incomplete picture. Products that are still operating provide valuable information, too.

SigmaXL's Weibull Analysis helps account for both failures and surviving units through censored data, giving you a more complete understanding of product reliability.

Sometimes, the most important data is what you might otherwise overlook.

Use Reliability/Weibull Analysis in SigmaXL V11: sigmaxl.com/Weibull.html

Did you know a cup of tea helped make statistical history?

In the 1920s, scientist Muriel Bristol claimed she could tell whether milk or tea had been poured into a cup first.

Statistician Ronald Fisher used this challenge to illustrate what became known as the famous "Lady Tasting Tea" experiment.

The experiment involved 8 cups: 4 prepared milk-first and 4 tea-first, presented in random order.

If someone were simply guessing, the probability of correctly identifying all 4 milk-first cups was just 1 in 70, or approximately 1.4%.

Fisher described the experiment in his 1935 book “The Design of Experiments”. The story later inspired David Salsburg's 2001 book “The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century”, which explores the fascinating history of modern statistics.

The same exact probability principles used in Fisher's experiment are also found in Fisher's Exact Test. Today, SigmaXL makes it easy to perform Fisher's Exact Test when comparing two proportions, particularly when sample sizes are small and traditional large-sample approximations may be unreliable.

Sometimes, even a simple cup of tea can teach us something about statistics!

Use the template in SigmaXL V11: sigmaxl.com/2PropTest.html

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Did you know the control chart started as a one-page memo?

On May 16, 1924, Walter Shewhart, working at Western Electric, sent his supervisor a short memorandum. About a third of the page was a simple diagram, now recognized as the first control chart.

Shewhart’s key insight was that every process varies, but not all variation is the same. Common-cause variation is the natural variation built into a process. Special-cause variation signals that something unusual has changed and may require investigation.

Reacting to common-cause variation as though it were a special cause often called tampering can actually increase variation and make a process worse.

More than 100 years later, control charts remain a foundation of Statistical Process Control (SPC) and Six Sigma.

SigmaXL makes it easy to create control charts directly in Excel, including Individuals, X-Bar & R/S, P, NP, C and U charts, with tests for special causes.

Try the charts in SigmaXL Version 11: sigmaxl.com/SigmaXLInforma…

Did you know the t-test has its roots in a brewery?

In the early 1900s, William Sealy Gosset was working at the Guinness brewery in Dublin, where he needed to make reliable decisions about things like barley and hops using very small samples.

The statistical methods available at the time weren’t well suited to that problem, so Gosset developed a new approach for analyzing small-sample data.

Because Guinness restricted employees from publishing work that could reveal company information, Gosset published his landmark 1908 paper under the pseudonym “Student.”

That’s why, more than a century later, we still call it Student’s t-distribution.

Today, SigmaXL makes it easy to run 1-sample, 2-sample, and paired t-tests directly in Excel, complete with assumption checks and useful graphical output.

A statistical method developed to solve a practical business problem is still helping people make better decisions today.

Learn more: sigmaxl.com/SigmaXLInforma…