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In statistics, distinguishing between a statistic and a parameter is crucial. A 'statistic' is a value that describes a sample, whereas a 'parameter' describes an entire population. This distinction helps us understand whether the data we observe provides a complete picture or just a part of it. For instance, if you survey 100 people about their favorite ice cream flavor out of a city's population, the average preference calculated from these 100 people is a statistic. However, if you had the data for everyone's favorite ice cream flavor in the entire city, that average would be a parameter.
In the given exercise, '146 garment workers died in the Triangle Shirtwaist Factory Fire' refers to the full number of workers who perished. Since this value describes everyone affected, it is a parameter, not just a sample of the workers.

The term 'population' in statistics is broader than it might first appear. A population is the entire group that you are interested in studying. It doesn't always refer to people; it can also include objects, events, measurements, and so forth. The main idea is that a population encompasses all members of the specific group you're analyzing.
In the context of the provided problem, the population is all the garment workers who died in the Triangle Shirtwaist Factory Fire. When we say 'population' here, we mean every single worker who lost their life in that event. By understanding the full population, we can classify our data appropriately, as seen in the earlier example where 146 is identified as a parameter because it covers the entire group that experienced the disaster.

A 'sample' in statistics refers to a subset of the population. This smaller group is chosen to represent the entire population, making it more manageable to collect and analyze data. Samples are often used because it is impractical or too costly to study the whole population.
For example, if you wanted to know the average height of students in a school, you might measure the height of a random group of 50 students and then use that data to make inferences about the entire student population. Thus, any measurements taken from this subset would be considered a 'statistic'.
Returning to the original problem, if we were given data such as '10 out of 146 garment workers in the Triangle Shirtwaist Factory Fire were under 18 years old,' this value would be a statistic. It represents just a part of the population (the sample), not the entire group.