![]() ![]() You can use inferential statistics to make estimates and test hypotheses about the whole population of 11th graders in the state based on your sample data. Example: Inferential statisticsYou randomly select a sample of 11th graders in your state and collect data on their SAT scores and other characteristics. If your sample isn’t representative of your population, then you can’t make valid statistical inferences. With inferential statistics, it’s important to use random and unbiased sampling methods. While descriptive statistics can only summarize a sample’s characteristics, inferential statistics use your sample to make reasonable guesses about the larger population. Most of the time, you can only acquire data from samples, because it is too difficult or expensive to collect data from the whole population that you’re interested in. You can then directly compare the mean SAT score with the mean scores of other schools. You can use descriptive statistics to get a quick overview of the school’s scores in those years. Example: Descriptive statisticsYou collect data on the SAT scores of all 11th graders in a school for three years. If you collect data from an entire population, you can directly compare these descriptive statistics to those from other populations. In descriptive statistics, there is no uncertainty – the statistics precisely describe the data that you collected. The variability concerns how spread out the values are.The central tendencyconcerns the averages of the values.The distribution concerns the frequency of each value.Using descriptive statistics, you can report characteristics of your data: Frequently asked questions about inferential statisticsĭescriptive versus inferential statisticsĭescriptive statistics allow you to describe a data set, while inferential statistics allow you to make inferences based on a data set.Estimating population parameters from sample statistics.Descriptive versus inferential statistics. ![]()
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