What is the difference between a percentile and a percent in compensation?

A percent is a ratio out of 100. A percentile represents position in a distribution. The 90th percentile means that 90% of values fall below it — not that it's 90% higher than something else.

Why do compensation professionals use percentiles?

Percentiles help benchmark roles across a market dataset. They provide meaningful signals on pay positioning without being skewed by outliers like averages.

Can percentiles be used for equity or bonus benchmarking?

Yes. You can use percentiles for any pay element: base salary, bonus, or equity — but be careful of how equity or variable pay can skew distributions.

Is the 90th percentile always a big jump from the median?

No. In tight datasets, the 90th might be very close to the 50th. Always look at the actual dollar amounts.

Are percentiles calculated the same across all survey providers?

No. Some surveys use different methodologies, smoothing, or rounding logic. Always check how they define percentile values.

Should I tell employees what percentile we target?

Usually not. Percentiles are complex and can be misinterpreted. It's better to share plain language explanations of your pay philosophy.

How can I use percentiles in my compensation planning process?

Use them to build ranges, anchor decisions, and model budget. But supplement with actual value analysis and sanity checks.

Is it okay to use narrow data cuts (e.g., SF-only, Series B-only)?

Yes, but beware of tight percentile spreads. Over-slicing can create misleading benchmarks. Broader cuts usually provide more reliable percentiles.

Why do percentiles sometimes feel misleading?

Because the difference between percentiles can be minimal — especially in tightly grouped data — even though the label (like “90th”) suggests otherwise.

Where can I learn more about using compensation surveys?

We recommend reading our full guide on how to leverage salary surveys to optimize your compensation strategy.

Why a Percentile Is Not a Percent — and Why That Matters in Compensation

In the world of compensation, it’s common to hear phrases like:

“We want to pay at the 90th percentile.”
“Let’s benchmark to the 75th percentile.”
“That role should sit around the 60th percentile.”

But here’s the problem:

Too many people treat percentiles like percentages, or assume they behave like standard deviations from a mean. That misunderstanding can lead to confusion, unrealistic pay expectations, and flawed decision-making.

Let’s break down why this matters — and how to use percentiles correctly when making compensation decisions.

So, What Is a Percentile?

A percentile is a rank or position in a dataset. It tells you the value below which a certain percentage of the data falls.

The 50th percentile (aka the median) means half the data is below that value.
The 90th percentile means 90% of the data points fall below that value.

Notice: it’s not about how much higher the 90th is than the 50th — it’s just saying where it lands in the distribution.

Why This Causes Confusion

Let’s say you’re looking at software engineer salaries:

Percentile	Salary
50th	$130,000
90th	$132,000

Now compare that to another dataset:

Percentile	Salary
50th	$130,000
90th	$195,000

Both are “paying at the 90th percentile” — but one implies a 2% increase over median, and the other implies a 50% increase.

That’s a massive difference in magnitude, and it’s why relying solely on percentiles without looking at actual numbers can be misleading. If your dataset is small or narrowly sliced, your percentile spreads may be extremely tight — leaving leaders, recruiters, or candidates wondering why the “90th” looks so… average.

But Wait — What About Standard Deviations?

In statistics, there’s a known relationship between percentiles and standard deviations if the data follows a normal distribution (the classic bell curve):

~68% of data falls within 1 standard deviation of the mean (roughly the 16th to 84th percentile)
~95% within 2 SDs
~99.7% within 3 SDs

But most compensation data isn’t perfectly normal. It’s skewed by factors like equity, signing bonuses, job title inflation, or small sample sizes. That’s why treating percentiles as proxies for standard deviation can get you into trouble.

Real-World Implications for Compensation Strategy

Let’s go back to that 90th percentile pay target.

If you don’t check the actual values behind that target:

You may offer far less than the market expects — despite paying at the “90th.”
You may blow your budget trying to hit a percentile that’s based on inflated outliers.
You may create internal confusion when percentiles don’t match expected percent differences.

That’s also why we usually don’t share percentiles with employees or candidates. It's a complex concept, and without understanding the data behind the number, percentiles can be more misleading than helpful.

Best Practices for Using Percentiles in Compensation

Look at the actual values, not just the percentile ranks. Understand the spread.
Use wide enough data cuts to avoid artificial compression between percentiles.
Avoid sharing percentiles externally unless you can explain the underlying dataset clearly.
Don’t assume percentiles = percent differences — they rarely do.
Double-check your expectations: if leaders expect a 90th percentile value to be “way above market,” make sure the data supports that.

Final Thought: Percentiles Require Context

Percentiles are a powerful tool in compensation — but only when used with the right context.

They’re not percentages. They’re not linear. And they’re not stand-ins for magnitude.

So the next time someone says, “Let’s pay at the 90th,” don’t just nod. Ask:

What does that actually mean — in dollars? In dataset size? In impact?

Because when it comes to pay, precision matters.