In statistics, a parameter is a numerical value that describes a characteristic of a population. It is a fixed, unknown value that we want to estimate using data from a sample.
Think of it like trying to figure out the average height of all students in a university. The parameter is the true average height of all students. We can't measure everyone, so we take a sample of students and use their heights to estimate the true average height of the entire student population.
Here are some common examples of parameters:
- Mean: Average value of a population (e.g., average height of all students)
- Standard deviation: Spread or variability of data in a population (e.g., how much the heights of students vary)
- Proportion: Percentage of a population that has a certain characteristic (e.g., percentage of students who are left-handed)
We use statistical methods like hypothesis testing and confidence intervals to estimate parameters based on sample data.
Practical Insights:
- Parameter estimation is crucial for making informed decisions. For example, a company might want to estimate the average income of its target market to determine the pricing of a new product.
- The accuracy of parameter estimates depends on the size and representativeness of the sample. A larger, more representative sample will generally lead to more accurate estimates.
