Estimation Statistics
An Estimation Statistics is a Data Analysis Framework that uses a combination of effect sizes, confidence intervals, precision planning, and meta-analysis to plan experiments, analyze data and interpret results.
- Example(s):
- Counter-Example(s):
- See: P-Value, Effect Size, Meta-Analysis, Statistical Hypothesis Testing.
References
2023
- (ChatGPT-OpenAi, 2023) ⇒ https://chat.openai.com Retrieved: 2023-4-16.
- Q: What is Estimation Statistics?
- A: Estimation statistics is a branch of statistics that involves estimating population parameters from sample data. It is used to make inferences about an unknown population based on a sample of data that is drawn from that population. Estimation statistics is used to calculate point estimates and confidence intervals for population parameters, such as means, proportions, and variances.
Point estimates are single values that are used to estimate a population parameter. For example, the sample mean is a point estimate of the population mean. However, point estimates are subject to sampling error, which is the difference between the sample estimate and the true population parameter.
Confidence intervals are a range of values that are likely to contain the true population parameter with a certain level of confidence. The level of confidence is typically set at 95%, which means that the true population parameter is expected to be within the confidence interval 95% of the time.
Estimation statistics is used in many fields, such as medicine, economics, and social sciences. It is used to estimate population parameters, make predictions, and test hypotheses. The accuracy of estimation statistics depends on the sample size, the sampling method, and the variability in the population.
2022
- (Wikipedia, 2022) ⇒ https://en.wikipedia.org/wiki/estimation_statistics Retrieved:2022-8-18.
- Estimation statistics, or simply estimation, is a data analysis framework that uses a combination of effect sizes, confidence intervals, precision planning, and meta-analysis to plan experiments, analyze data and interpret results. It complements hypothesis testing approaches such as null hypothesis significance testing (NHST), by going beyond the question is an effect present or not, and provides information about how large an effect is. [1] Estimation statistics is sometimes referred to as the new statistics.[1]
The primary aim of estimation methods is to report an effect size (a point estimate) along with its confidence interval, the latter of which is related to the precision of the estimate.[2] The confidence interval summarizes a range of likely values of the underlying population effect. Proponents of estimation see reporting a P value as an unhelpful distraction from the important business of reporting an effect size with its confidence intervals, and believe that estimation should replace significance testing for data analysis.
- Estimation statistics, or simply estimation, is a data analysis framework that uses a combination of effect sizes, confidence intervals, precision planning, and meta-analysis to plan experiments, analyze data and interpret results. It complements hypothesis testing approaches such as null hypothesis significance testing (NHST), by going beyond the question is an effect present or not, and provides information about how large an effect is. [1] Estimation statistics is sometimes referred to as the new statistics.[1]
- ↑ 1.0 1.1 Cumming, Geoff (2011). Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. New York: Routledge. ISBN 978-0415879675.
- ↑ Cohen, Jacob (1990). "Things I have learned (so far)". American Psychologist. 45 (12): 1304–1312. doi:10.1037/0003-066x.45.12.1304.