However, using probability sampling methods reduces this uncertainty.ĭiscover proofreading & editing Estimating population parameters from sample statistics For this reason, there is always some uncertainty in inferential statistics. Sampling error arises any time you use a sample, even if your sample is random and unbiased. This creates sampling error, which is the difference between the true population values (called parameters) and the measured sample values (called statistics). Since the size of a sample is always smaller than the size of the population, some of the population isn’t captured by sample data. 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 or generalize. 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.
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