What is statistics? A beginner’s guide to sampling and data

Every day, someone uses data to make a decision for you. A healthcare network forecasts patient admission rates to allocate medical staff. A financial institution adjusts interest rates to manage market risk. A video streaming platform changes its algorithm to recommend different creators to your feed. The skill that lets you weigh any of those claims is statistics. You do not need to be a “math person” to build it. You need a handful of ideas about where data comes from and how it gets collected. That is where ASU’s Universal Learner Course in statistics, STP 226: Elements of Statistics, begins. You can start it for $25, with no application required.

Why learning statistics is a 2026 career skill

It is easy to treat statistics as a box to check for a degree. The job market tells a different story. The U.S. Bureau of Labor Statistics projects data scientist employment to grow about 34% from 2024 to 2034, one of the fastest rates of any occupation. The demand reaches past specialist roles. In a 2026 survey of more than 500 of business leaders, 88% said basic data literacy is essential for day-to-day work, yet about 60% reported a data skills gap on their teams.

Put plainly: the ability to look at a chart or a “studies show” claim and ask where the data came from is becoming a baseline professional skill. A few foundational ideas are at the foundation of this skill.

Where data comes from

Data shapes much of how we live, from the laws that govern us to the prices we pay. Before you can analyze data, you have to understand what data is and how it gets gathered responsibly. 

A few ideas do most of the work: the difference between a population and a sample, how sampling decides what you can trust, the gap between correlation and causation and the type of data you are actually working with. 

Let’s review each one.

What is the difference between a population and a sample?

A population is every individual you care about, say all 50,000 employees at a company. A sample is the smaller group you actually measure, maybe 500 of them. 

Almost no study measures the full population. It costs too much or cannot be done. So researchers measure a sample and use it to make a claim about the whole.

A number that describes the full population is a parameter. The same number calculated from your sample is a statistic. Once that clicks, you read the news differently. “73% of Americans believe X” was never measured across all Americans. 

It came from a sample. The question worth asking next is whether that sample was chosen well.

How does sampling work, and what is sampling error?

How you choose your sample decides whether your conclusion holds. A poll of only your friends will never represent a country, no matter how many friends you ask.

Sampling error is the natural gap between what your sample shows and the true population value. It never goes away, but sound sampling methods keep it small and measurable. That is why a well-designed survey of 1,000 people can reflect millions, while a sloppy survey of 100,000 can be worthless. Learning to spot the difference is one of the most useful things statistics teaches.

Correlation vs causation, and the data you actually need

Two questions trip up even experienced professionals. The first is correlation versus causation. Two things moving together does not mean one causes the other. What separates the two is study design: researchers use controlled, randomized methods to support a cause-and-effect claim, and you can learn to tell when a claim hasn’t earned it.

The second is data type. Qualitative data describes categories, like eye color or job title. Quantitative data is numerical, like salary or age. Quantitative data splits again: discrete data is countable, like the number of children in a household, and continuous data is measurable on a scale, like height. Choosing the right type up front decides which analyses are even possible later. Get these distinctions right and the rest of statistics, from graphs to averages to hypothesis tests, falls into place more easily.

Get these distinctions right and the rest of statistics, from graphs to averages to hypothesis tests, falls into place more easily. Mastering these principles opens the door to advanced analytical work, and you can practice applying them directly to real-world data sets by enrolling in STP226: Elements of Statistics, one of ASU’s Universal Learner Courses. You can start learning for $25 and choose to pay $400 to add the course to an official ASU transcript only if you like your final grade.

Who teaches STP226?

STP 226 is taught by Natalie Welcome, an instructor in the School of Applied Sciences and Arts within ASU’s College of Integrative Sciences and Arts. Welcome holds a Master of Arts in mathematics and a Doctor of Philosophy in curriculum and instruction from Texas Tech University, and came to mathematics from industrial engineering — a route she credits with her skill for helping students who once found the subject difficult come to appreciate it.

What the course actually looks like

STP 226 is a 3-credit course you can take session-based over 8 weeks or fully on-demand at your own pace. It combines short instructional videos, quick check quizzes that reinforce each concept, and a hands-on software tutorial, with grades based on practice quizzes and exams. The credit satisfies ASU’s Quantitative Reasoning general studies requirement and appears on an ASU transcript exactly as it would for an on-campus student. College Algebra (MAT 117) or College Mathematics (MAT 114) are suggested for success, though neither is required to enroll.

Do you need to be good at math to take statistics?

No. The starting point is concepts, what data is, where it comes from and how it gets collected, more than heavy computation. The math you do need is supported with step-by-step software tutorials, and the course is built for learners with little or no prior experience in statistics.

How much does ASU’s online statistics course cost?

You start STP 226 for $25, with no application. You decide whether to add the course to your ASU transcript for credit only after you see your grade, and that step costs $400. If statistics turns out not to be for you, you have spent $25 to find out. Full details are on the ULC pricing page.

Start building your data skills

Statistics is the skill of thinking clearly in a world that runs on data. It serves as a first step on a path that can lead to early college credit and accelerate your progress toward an ASU degree.

Ready to begin? Enroll in STP 226: Elements of Statistics for $25 and start building the skill.