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Uncertainty: Overview
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Interpretation of Confidence Intervals
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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Uncertainty: Confidence Intervals
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Confidence Intervals
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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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Uncertainty in Measurement: Accuracy and Precision
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
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Estimation of the Physical Quantities
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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关于确定性的说法是否使估计变得不那么确定?
Karl Halvor Teigen1, Marie Juanchich2
1University of Oslo, Norway.
Cognition
|August 14, 2024
概括
宣称陈述是"确定的"可以悖论地降低它们所感知的可信性和准确性. 无条件的陈述往往被视为更值得信赖和不那么模糊,影响传达信心的沟通策略.
科学领域:
- 认知心理学 认知心理学
- 语言学的语言学.
- 决策科学 决策科学 决策科学
背景情况:
- 口头概率表达式通常将"确定性"置于最高水平,暗示概率接近1.
- 很少有研究将明确的确定性声明与简单,无条件的陈述进行了比较.
研究的目的:
- 调查明确的确定性要求如何影响人们对可信度,准确性和证据支持的看法.
- 将明确的确定性陈述与简单的,没有标记的声明性陈述进行比较.
主要方法:
- 进行了9项涉及2784名参与者的研究.
- 参与者在可信度,准确性和证据基础等级上对具有或没有明确确定性标记的陈述进行了评估.
主要成果:
- 声称确定性的陈述往往被认为不那么值得信赖,可靠,并且持有较低的信心,而不是没有标记的陈述.
- 确定性陈述被发现更为模两可,可能表明未定义的上限,并影响风险选择场景中的解释.
- 非个人确定性 ("它是确定的") 被认为比个人确定性 ("我确定") 更准确,特别是对于未来的预测.
结论:
- 明确的确定性要求可能会破坏可信度和准确性,这与常见的假设相反.


