Examples of statistical power in the following topics:
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- Statistical power helps us answer the question of how much data to collect in order to find reliable results.
- Statisticians provide the answer in the form of statistical power.
- Statistical power may depend on a number of factors.
- Increasing sample size is often the easiest way to boost the statistical power of a test.
- Discuss statistical power as it relates to significance testing and breakdown the factors that influence it.
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- Cohen, J. (1988) Statistical Power Analysis for the Behavioral Sciences (second ed.).
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- What type of analysis would be done for each type of design and how would the choice of designs affect power?
- The latter would be more powerful.
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- Statistics teaches people to use a limited sample to make intelligent and accurate conclusions about a greater population.
- This ability is provided by the field of inferential statistics.
- Those proceeding to higher education will learn that statistics is an extremely powerful tool available for assessing the significance of experimental data and for drawing the right conclusions from the vast amounts of data encountered by engineers, scientists, sociologists, and other professionals in most spheres of learning.
- In today's information-overloaded age, statistics is one of the most useful subjects anyone can learn.
- Statistics are often used by politicians, advertisers, and others to twist the truth for their own gain.
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- Several factors affect the power of a statistical test.
- In other words, factors that affect power.
- Figure 1 shows that the larger the sample size, the higher the power.
- There is a trade-off between the significance level and power: the more stringent (lower) the significance level, the lower the power.
- Figure 3 shows that power is lower for the 0.01 level than it is for the 0.05 level.
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- Descriptive statistics can be manipulated in many ways that can be misleading, including the changing of scale and statistical bias.
- Descriptive statistics can be manipulated in many ways that can be misleading.
- Bias is another common distortion in the field of descriptive statistics.
- The following are examples of statistical bias.
- Descriptive statistics is a powerful form of research because it collects and summarizes vast amounts of data and information in a manageable and organized manner.
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- Draw a graph, calculate the test statistic, and use the test statistic to calculate the p-value.
- (A z-score and a t-score are examples of test statistics. )
- Remember that the quantity 1− is called the Power of the Test.
- A high power is desirable.
- If the power is low, the null hypothesis might not be rejected when it should be.
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- If we take a very large sample, we might find a statistically significant difference but the magnitude might be so small that it is of no practical value.
- This video was created by OpenIntro (openintro.org) and provides an overview of the content in Section 4.6 of OpenIntro Statistics, which is a free statistics textbook with a $10 paperback option on Amazon.
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- While we still say that difference is statistically significant, it might not be practically significant.
- Statistically significant differences are sometimes so minor that they are not practically relevant.
- With these important pieces of information, she would choose a sufficiently large sample size so that the power for the meaningful difference is perhaps 80% or 90%.
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- List 3 measures one can take to increase the power of an experiment.
- Explain why your measures result in greater power.
- Hint: the power of a one-tailed test at 0.05 level is the power of a two-tailed test at 0.10.
- (a) What is the probability that Alan will be able convince Ken that his coin has special powers by finding a p value below 0.05 (one tailed).