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相关概念视频

Trial and Error and Algorithm01:12

Trial and Error and Algorithm

150
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
150
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.4K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.4K
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

169
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
169
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

3.5K
A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
3.5K
Testing a Claim about Mean: Known Population SD01:11

Testing a Claim about Mean: Known Population SD

2.8K
A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
2.8K

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相关实验视频

Updated: Jul 26, 2025

Measuring the Switch Cost of Smartphone Use While Walking
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小试验问题 小试验问题

Jean Raymond1, Tim E Darsaut2, Johanna Eneling3

  • 1Department of Radiology, Service of Neuroradiology, Centre Hospitalier de L'Université de Montréal (CHUM), Montreal, QC, H2X 0C1, Canada. jean.raymond@umontreal.ca.

Trials
|June 22, 2023
PubMed
概括

许多医学试验太小而不可靠. 研究人员应该比较患者的结果,而不是平均值,以确保试验结果是有意义的,适用于现实世界的实践.

关键词:
两体化 (Dichotomization) 是一种方法.患者的数量 患者的数量与安慰剂控制的外科试验.实用性试验是指实用性的试验.连续变量问题 连续变量问题样本的大小 样本大小外科手术 手术手术试验方法论试验方法论试验规模试验规模

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相关实验视频

Last Updated: Jul 26, 2025

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科学领域:

  • 临床试验 临床试验
  • 生物统计学 生物统计学
  • 医学研究方法学 医学研究方法学

背景情况:

  • 许多随机试验的实力不足,质疑其结论的有效性.
  • 小样本大小限制了医学和外科干预发现的概括性.

研究的目的:

  • 为了说明小型临床试验的问题,使用从脊椎造形术研究中获得的功率计算.
  • 讨论确定有意义的试验结果的适当样本大小的统计方法.

主要方法:

  • 对五项Cochrane审查的脊椎造形与安慰剂试验的功率计算分析.
  • 检查统计方法,包括使用连续变量与二元结果的研究.

主要成果:

  • 计划为每组23-71名患者进行的脊椎造形试验太小了.
  • 在五项研究中,有四项不适当地使用连续疼痛变量 (视觉模拟尺度) 来计算样本大小.
  • 为了比较实现值结果的患者比例,需要进行更大规模的试验,但可以获得更有意义的结果.

结论:

  • 大多数安慰剂对照的脊椎造形试验由于比较连续变量的平均值而不足.
  • 随机试验必须足够大,以反映患者和实践的多样性.
  • 启发临床实践的试验需要对每个患者的结果进行比较,并采用适当大小的样本.