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Making InFEREnCES from Random Samples\begin{tabular}{|c|c|}\hline ims to Know: & Definition: \\\hline Random Sample & \begin{tabular}{l} When collecting \\smaller group of subiects from that population that have an equal \\of being chosen to participate.\end{tabular} \\\hline \begin{tabular}{l} Inference \\from a Random Sample\end{tabular} & \\\hline\end{tabular}
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Q. Making InFEREnCES from Random Samples\begin{tabular}{|c|c|}\hline ims to Know: & Definition: \\\hline Random Sample & \begin{tabular}{l} When collecting \\smaller group of subiects from that population that have an equal \\of being chosen to participate.\end{tabular} \\\hline \begin{tabular}{l} Inference \\from a Random Sample\end{tabular} & \\\hline\end{tabular}
- Random Sampling: Random samples are used to make inferences about a larger population, because every member of the population has an equal chance of being included in the sample.
- Collect Data: To make an inference from a random sample, you collect data from the sample and then use that data to make generalizations about the whole population.
- Make Generalizations: For example, if you have a random sample of students' test scores, you can infer the average test score for all students in the school.
- Sample Size Importance: It's important to remember that the larger the sample size, the more reliable the inference will be, because it's more likely to represent the population accurately.
- Sampling Error: However, there's always a chance of sampling error, which means the sample might not perfectly represent the population, no matter how random it is.
