Examples of significance level in the following topics:
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- Choosing a significance level for a test is important in many contexts, and the traditional level is 0.05.
- However, it is often helpful to adjust the significance level based on the application.
- Is there good reason to modify the significance level in such an evaluation?
- A slightly larger significance level, such as α = 0.10, might be appropriate.
- Choose a small significance level, such as α = 0.01.
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- If a test of significance gives a $p$-value lower than or equal to the significance level, the null hypothesis is rejected at that level.
- A fixed number, most often 0.05, is referred to as a significance level or level of significance.
- If a test of significance gives a $p$-value lower than or equal to the significance level, the null hypothesis is rejected at that level.
- The lower the significance level chosen, the stronger the evidence required.
- The choice of significance level is somewhat arbitrary, but for many applications, a level of 5% is chosen by convention.
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- This is denoted by the significance level of the model.
- Within the social sciences, a significance level of 0.05 is often considered the standard for what is acceptable.
- If the significance level is between 0.05 and 0.10, then the model is considered marginal.
- To see if weight is a "significant" predictor of height, we would look at the significance level associated with weight.
- Again, significance levels of 0.05 or lower would be considered significant, and significance levels between 0.05 and 0.10 would be considered marginal.
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- If a test of significance gives a $p$-value lower than or equal to the significance level, the null hypothesis is rejected at that level .
- Such results are informally referred to as 'statistically significant (at the $p=0.05$ level, etc.)'.
- For example, if someone argues that "there's only one chance in a thousand this could have happened by coincidence", a $0.001$ level of statistical significance is being stated.
- For example, we could measure two different one-cup measuring cups enough times to find that their volumes are statistically different at a significance level of $0.001$.
- The difference in this case is statistically significant at a certain level, but not important.
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- Popular levels of significance are 10% (0.1), 5% (0.05), 1% (0.01), 0.5% (0.005), and 0.1% (0.001).
- If a test of significance gives a p-value lower than or equal to the significance level , the null hypothesis is rejected at that level.
- Such results are informally referred to as 'statistically significant (at the p = 0.05 level, etc.)'.
- The lower the significance level chosen, the stronger the evidence required.
- The choice of significance level is somewhat arbitrary, but for many applications, a level of 5% is chosen by convention.
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- In this chapter of this textbook, we will always use a significance level of 5%, α = 0.05
- But the table of critical values provided in this textbook assumes that we are using a significance level of 5%, α = 0.05.
- If the p-value is less than the significance level (α = 0.05):
- If the p-value is NOT less than the significance level (α = 0.05)
- The p-value, 0.026, is less than the significance level of α = 0.05
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- The proportion of confidence intervals that contain the true value of a parameter will match the confidence level.
- The desired level of confidence is set by the researcher (not determined by data).
- If a corresponding hypothesis test is performed, the confidence level is the complement of respective level of significance (i.e., a 95% confidence interval reflects a significance level of 0.05).
- In applied practice, confidence intervals are typically stated at the 95% confidence level.
- However, when presented graphically, confidence intervals can be shown at several confidence levels (for example, 50%, 95% and 99%).
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- The probability value below which the null hypothesis is rejected is called the α level or simply α.
- It is also called the significance level.
- Do not confuse statistical significance with practical significance.
- The alternative approach (favored by the statisticians Neyman and Pearson) is to specify an α level before analyzing the data.
- Therefore, if the 0.05 level is being used, then probability values of 0.049 and 0.001 are treated identically.
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- An organization can have many different managers, across many different titles, authority levels, and levels of the management hierarchy.
- Examples of top-level managers include a company's board of directors, president, vice-president and CEO; examples of middle-level managers include general managers, branch managers, and department managers; examples of low-level managers include supervisors, section leads, and foremen.
- In addition, top-level managers play a significant role in the mobilization of outside resources.
- Middle-level managers devote more time to organizational and directional functions than top-level managers.
- Also referred to as first-level managers, low-level managers are role models for employees.
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- The statistical significance of the results depends on criteria set up by the researcher beforehand.
- However, conventional thresholds for significance may vary depending on disciplines and researchers.
- For example, health sciences commonly settle for 10% ($\text{sig} \leq 0.10$), while particular researchers may settle for more stringent conventional levels, such as 1% ($\text{sig} \leq 0.01$).
- Assuming a conventional 5% level of significance ($\text{sig} \leq 0.05$), all tests are, thus, statistically significant.
- Examine the idea of statistical significance and the fundamentals behind the corresponding tests.