Population is a statistical term that designates the pool from which a sample is drawn for a study. Any selection grouped by a common feature can be considered a population. A sample is a statistically significant portion of a population.

### Key Takeaways

- A population is the entire group on which data is being gathered and analyzed.
- It's generally difficult in terms of cost and time to gather the data needed on an entire population so samples are often used to make inferences.
- A sample of a population must be randomly selected for the results of the study to accurately reflect the whole.
- A valid statistic can be drawn from either a sample or a study of an entire population.

## Understanding Populations

Statisticians, scientists, and analysts prefer to know the characteristics of every entity in a population to draw the most preciseconclusions possible. This is impossible or impractical most of the time, however, because population sets tend to be quite large.

A sample of a population must usually be taken because the characteristics of every individual in a population can't be measured due to constraints of time, resources,and accessibility.

### Note

The term "individual" doesn't always mean a person in statistics. An individual is a single entity in the group being studied.

There's no real way to gather data on all the great white sharks in the ocean, which is a population. Finding and tagging each one isn't feasible. Marine biologists instead tag the great whites they can as a sample. They begin collecting information on them to make inferences about the entire population of great whites. This is a random sampling approach because the initial encounters with tagged great whites are entirely random.

A valid statistic can be drawn from either a sample or a study of an entire population. The objective of a random sample is to avoid bias in the results. A sample is random when every member of the whole population has an equal chance to be selected to participate.

## How to Measure a Population

The difficulty in measuring a population lies in whatever you're attempting to analyze and what you're trying to accomplish. Data must be collected through surveys, measurements, observation, or other methods. Gathering the data on a large population generally isn't done because of the costs, time, and resources required to obtain it.

All the doctors with patients who could use Drug XYZ in the U.S. likely weren't contacted to confirm this if you see an advertisem*nt that claims, "62% of doctors recommend XYZ for their patients!" Rather 62% of the doctors who responded to the several hundred or thousands of surveys that were sent out responded that they would recommend XYZ. This is a population sample.

## Population and Investing

A parameter is a characteristic of a population. A statistic is a characteristic of a sample and samples can only result in inferences about a population characteristic. Inferential statistics allow you to make an educated guess about a population parameter based on a statistic computed from a sample randomly drawn from that population.

Statistics such asaveragesor means andstandard deviations are referred to as population parameters when they're taken from populations. Many such as a population's mean and standard deviation are represented by Greek letterslike µ (mu) and σ (sigma). These statistics are inferential in nature much of the time because samples are used rather than populations.

You don't have to use statistical inference if you have all the data for the population being studied because you won't have to use a sample of the population.

Market and investment analysts use statistics to analyze investment data and make inferences about the market, a specific investment, or an index. Financial analysts can evaluate an entire population in some cases because price data has been recorded for decades. The price of every publicly traded stock could be analyzed for a total market evaluation because the prices are recorded. This is a population in terms of investment analysis.

An analyst can calculate parameters with all this data but the parameters used by analysts are only occasionally used in the same way that statisticians and scientists use them.

Some of the parameters you might see used by investment analysts, statisticians, and scientists and their differences are:

Investment Analysts

**Alpha**: The excess returns of an asset compared to a benchmark**Standard Deviation**: Average amount of variability in prices, used to measure volatility and risk**Moving Average**: Used to smooth out short-term price fluctuations to indicate trends**Beta**: Measures the performance of an investment/portfolio against the market as a whole

Statisticians and Scientists

**Alpha**: The probability of making a Type I error, or rejecting the null hypothesis when it is true**Standard Deviation**: Average amount of variability in data**Moving Average**: Smooths out short-term fluctuations in data values**Beta**: The probability of making a Type II error, or incorrectly failing to reject the null hypothesis

## What Is a Population Mean?

A population mean is the average of whatever value you're measuring in a given population.

## What Are Two Examples of Population?

An example of a population might be all green-eyed children in the U.S. under age 12. Another could be all the great white sharks in the ocean.

## What Is the Best Example of a Population?

Imagine you're a teacher trying to see how well your fifth-grade math class did on a standardized test compared to all fifth-graders in the U.S. The population would be all fifth-grade math scores in the country.

## The Bottom Line

A population is the statistical pool being studied from which data is extracted. Populations can be difficult to gather data on, especially if the studied topic is expansive and widely dispersed. Studying humans is an excellent example. There's no way to gather data on every brown-eyed person in the world so random sampling is the only way to infer anything about that population.

Populations in investment analysis are generally specific types of assets being analyzed. These data sets are generally small in statistical terms and easy to acquire because they've been recorded, unlike data on living organisms which is much more difficult to obtain.