Sampling Methods

Sampling Methods are part of the GED science test.

This lesson is a part of Onsego GED Prep.

Video Transcription

Sampling is a fundamental tool in scientific research, allowing scientists to draw conclusions about large populations based on studying smaller subsets.

When scientists conduct research, they often rely on samples—a smaller portion of the entire group they’re studying.

Studying the entire group can be impractical. Imagine trying to count every leaf in a forest to study tree health or analyzing every grain of sand on a beach to understand erosion patterns.

That’s why scientists use samples.

Well-designed scientific experiments aim to control possible sources of bias.

To draw accurate conclusions from sample data, the sample must be large enough and representative of the whole group.

To make valid inferences about a larger group based on samples, scientists must follow certain guidelines. A major concern is that the sample may not accurately reflect the whole group. An unrepresentative sample introduces bias into the study results.

One way to prevent bias is to select a large enough sample size to reduce the probability that the sample differs significantly from the whole group by random chance.

Imagine scientists studying soil quality in a large agricultural field. Which of the following sample sizes is more likely to lead to valid conclusions about the field’s soil?

Option 1: Taking just one soil sample from one spot in the field.
Option 2: Taking 10 soil samples from different areas across the field.

The correct answer is Option 2. If scientists took only one sample, it might happen to be from a particularly fertile spot, leading to the incorrect conclusion that the entire field has high-quality soil. However, taking 10 samples from different locations is likely to provide a more accurate representation of the field as a whole. This reduces the chance of basing conclusions on an unrepresentative sample.

Scientists also use randomization to ensure that the experimental and control groups are as similar as possible, except for the treatment being tested. For instance, if scientists wanted to determine whether a new fertilizer affects crop yield, they would:

1. Take samples of soil from the field.
2. Randomly assign half of the samples to a control group (no fertilizer).
3. Assign the other half to an experimental group (apply fertilizer).

Then, they would measure crop yield in both groups to see if there’s a statistically significant difference.

When evaluating the quality of a scientific study, consider whether the data were gathered from a sample similar to the population of interest.

By carefully designing their sampling methods, scientists can ensure that their research findings are valid and reliable.

Last Updated on October 20, 2025.