Up to this point, we’ve been focused on finding ways to summarize, describe, and visualize statistical data. If we want to make the most of our data, however, we can’t stop there-we want to be able to make reasonable inferences about entire populations using the sample information at our disposal.
To illustrate briefly: an organization might infer that a product is in high demand among its customers by analyzing the buying habits of a representative group of individuals. A school, in turn, might infer that a particular professor is performing poorly if most of their students are scoring low on quarterly exams. In essence, it’s through making these inferences that organizations can answer pressing business questions and develop effective strategies.
Let’s break down the concepts of sampling and inference in the specific context of business statistics:
The Basics of Sampling and Inference
Sampling is the process of selecting a small group (the sample) from a larger group (the population) to make observations and draw conclusions about the entire population. The idea is that it’s often impractical or impossible to study the entire population, so we study a sample instead. Using one of the earlier examples, a certain business might serve 10,000 customers or more, but studying that population may require us to take only a limited number of observation-say around 200.
Meanwhile, inference is the process of making educated guesses or predictions about a population based on data from a sample. Inference is what helps businesses make truly data-driven decisions rather than strategizing based on guesswork. It also empowers them to understand trends and patterns within their market or customer base, and it’s invaluable for forecasting future outcomes.
Selecting Valid Samples
One valuable point to remember is that for a statistical sample to be valid and thus to produce valid inferences, it needs to be representative of the population. A sample is considered representative if it accurately reflects the characteristics and diversity of the entire population. This means that the sample should be a smaller, proportionate version of the population, including all the relevant attributes, variations, and subgroups within it.
A representative sample allows you to make the most accurate possible generalizations about the population. If the sample isn’t representative, the conclusions you draw from it have a higher chance of being incorrect or misleading. Bias can also occur when certain segments of the population are underrepresented or overrepresented in a sample. Representative samples minimize this for more reliable results.
How Sampling Is Done
Statisticians use many methods to choose a sample, but here are two of the most common ones:
- Simple random sampling – Every member of the population has an equal chance of being selected for the sample. This eliminates bias and makes the sample more likely to be representative of the entire population. Simple random sampling is the easiest and most straightforward way to get a representative sample.
- Stratified sampling – The population is divided into subgroups known as strata based on certain characteristics relevant to the study, such as age, income, or other variables. From there, random samples are taken from each subgroup. This ensures that each subgroup is properly represented in the sample.
Sampling Distribution and Its Value
A sampling distribution is the probability distribution of a given statistic based on multiple random samples. In simpler terms, it’s a way to understand how a statistic (like the sample mean or sample proportion) behaves when you take many samples from the same population.
Imagine you repeatedly take random samples from a population and calculate a statistic, such as the mean. Each time you take a sample, you might get a slightly different statistic. The sampling distribution is the collection of all those statistics.
The sample mean is one of the most common statistics used for sampling distribution. If you’re sampling from a population with a known standard deviation, and you have a sufficiently large sample size (usually 𝑛 > 30), you can represent the mean of the sampling distribution with the following equation:
Meanwhile, the standard deviation of the sampling distribution (also known as the “standard error”) is calculated using the following equation:
In both cases:
- μ is the population mean.
- 𝜎 is the population standard deviation.
- 𝑛 is the sample size.
is the random variable representing the sample mean.
It’s important to be familiar with sampling distribution because, to a great extent, it provides a theoretical basis for statistical inference techniques. It allows statisticians to estimate population parameters and assess the accuracy of these estimates. Moreover, sampling distribution illustrates how a statistic varies from sample to sample. This variability is inherent in any sampling process, and understanding it helps statisticians evaluate how reliable their estimates are.
A Concrete Illustration of Sampling Distribution
Let’s visualize the concept of sampling distribution more concretely using the scenario provided earlier. Say an organization is aiming to examine the buying habits of its customers and thereby determine the demand for a specific product. We can peg the total population at around 10,000 customers, and the sample size at 200 customers. From there, we’ll need to perform the following steps:
1. Define the population and the statistic of interest.
- Our population is all 10,000 customers.
- Our statistic of inference is the average number of products that customers buy. Let’s call this the “average purchase.â€
2. Take a single sample.
- Randomly select 200 customers from the 10,000.
- Calculate the average number of products that these 200 customers purchase. We’ll call this sample mean xÌ„1.
3. Repeat the sampling process.
- To create a sampling distribution, repeat the process of taking random samples of 200 customers and calculating the sample mean multiple times.
- For illustration, let’s say we take 1,000 different samples of 200 customers each.
4. Collect the sample means.
- After taking 1,000 samples, we will have 1,000 sample means.
- We can represent these sample means as follows:
The collection of these 1,000 sample means forms the sampling distribution for this particular scenario.
Moving Forward from the Fundamentals
A comprehensive understanding of sampling and inference will give you a strong foundation for understanding market trends and optimizing your business operations accordingly. This knowledge also sets the stage for more advanced statistical analysis such as hypothesis testing, which we’ll be covering in the next few lessons.
