In this Grade 7 Pengi Math lesson from Chapter 9: Statistics, students learn to distinguish between a population and a sample, and explore why representative samples are essential for making valid inferences. The lesson covers methods for generating simple random samples to minimize bias and introduces the concept of sampling variability — why different random samples can produce different results.
Section 1
Defining Population and Sample
A population is the complete set of all individuals or items that we want to study and draw conclusions about. A sample is a subset of the population that is actually observed or measured to gather data.
Section 2
Random Sampling
A random sample is a subset of individuals (a sample) chosen from a larger set (a population). Each individual is chosen randomly and entirely by chance, such that each individual has the same probability of being chosen at any stage during the sampling process, and each subset of individuals has the same probability of being chosen for the sample as any other subset of individuals. A simple random sample is an unbiased surveying technique.
Think of this as the fairest way to pick a small group to represent a big one. Everyone has an equal chance of being chosen, like drawing names from a hat. This helps make sure your sample truly reflects the whole population.
Section 3
Identifying Biased and Unbiased Sampling Methods
A sampling method is biased if it systematically favors certain groups or excludes parts of the population. A sampling method is unbiased if it gives every member of the population an equal chance of being selected and produces a representative sample.
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Section 1
Defining Population and Sample
A population is the complete set of all individuals or items that we want to study and draw conclusions about. A sample is a subset of the population that is actually observed or measured to gather data.
Section 2
Random Sampling
A random sample is a subset of individuals (a sample) chosen from a larger set (a population). Each individual is chosen randomly and entirely by chance, such that each individual has the same probability of being chosen at any stage during the sampling process, and each subset of individuals has the same probability of being chosen for the sample as any other subset of individuals. A simple random sample is an unbiased surveying technique.
Think of this as the fairest way to pick a small group to represent a big one. Everyone has an equal chance of being chosen, like drawing names from a hat. This helps make sure your sample truly reflects the whole population.
Section 3
Identifying Biased and Unbiased Sampling Methods
A sampling method is biased if it systematically favors certain groups or excludes parts of the population. A sampling method is unbiased if it gives every member of the population an equal chance of being selected and produces a representative sample.
Book overview
Jump across lessons in the current chapter without opening the full course modal.
Continue this chapter