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Questions and Answers
What does the null hypothesis predict regarding statistical differences?
What does the null hypothesis predict regarding statistical differences?
- It predicts no statistical difference exists between groups. (correct)
- It states that researchers will always find a significant result.
- It predicts a statistical difference exists between groups.
- It assumes all variables have a direct correlation.
Which of the following accurately describes a Type I error?
Which of the following accurately describes a Type I error?
- Rejecting the null hypothesis when it is true. (correct)
- Accepting the alternative hypothesis when the data is inconclusive.
- Failing to reject the null hypothesis when it is true.
- Not conducting a significance test when required.
What is the significance level commonly used in hypothesis testing?
What is the significance level commonly used in hypothesis testing?
- 0.01
- 0.05 (correct)
- 0.20
- 0.10
Which term describes the probability of obtaining a statistically significant result if the null hypothesis is true?
Which term describes the probability of obtaining a statistically significant result if the null hypothesis is true?
Which of the following represents a Type II error?
Which of the following represents a Type II error?
In hypothesis testing, what is indicated by a low p-value (less than 0.05)?
In hypothesis testing, what is indicated by a low p-value (less than 0.05)?
What does statistical power in hypothesis testing refer to?
What does statistical power in hypothesis testing refer to?
What is the consequence of setting a very low significance level in hypothesis testing?
What is the consequence of setting a very low significance level in hypothesis testing?
What happens to the probability of committing a Type II error as sample size increases?
What happens to the probability of committing a Type II error as sample size increases?
What does statistical power refer to in hypothesis testing?
What does statistical power refer to in hypothesis testing?
At which level does significance typically represent the threshold for determining Type I error?
At which level does significance typically represent the threshold for determining Type I error?
What is the relationship between sample size and statistical power?
What is the relationship between sample size and statistical power?
Which statement is NOT true regarding parameters in biological research?
Which statement is NOT true regarding parameters in biological research?
What is a Type I error in hypothesis testing?
What is a Type I error in hypothesis testing?
What does the standard error of the mean (SEM) indicate?
What does the standard error of the mean (SEM) indicate?
How is statistical power defined in the context of hypothesis testing?
How is statistical power defined in the context of hypothesis testing?
In hypothesis testing, what does a significance level of $0.05$ imply about the corresponding confidence level?
In hypothesis testing, what does a significance level of $0.05$ imply about the corresponding confidence level?
What does it mean if the error bars representing the 95% confidence interval do not overlap?
What does it mean if the error bars representing the 95% confidence interval do not overlap?
Which situation describes a Type II error?
Which situation describes a Type II error?
What characterizes the significance level in hypothesis testing?
What characterizes the significance level in hypothesis testing?
Which statement accurately describes Type I Error?
Which statement accurately describes Type I Error?
What is the impact of increasing the sample size on the statistical power of a test?
What is the impact of increasing the sample size on the statistical power of a test?
What is meant by the probability of committing a Type I error?
What is meant by the probability of committing a Type I error?
If a study has high statistical power, which of the following is likely true?
If a study has high statistical power, which of the following is likely true?
Which describes Type II Error in hypothesis testing?
Which describes Type II Error in hypothesis testing?
Which outcome does a researcher hope to avoid when conducting an experiment?
Which outcome does a researcher hope to avoid when conducting an experiment?
Which of the following statements is correct regarding significance levels?
Which of the following statements is correct regarding significance levels?
In hypothesis testing, which scenario is evidence of a Type I error?
In hypothesis testing, which scenario is evidence of a Type I error?
How do confidence intervals contribute to understanding measurement error?
How do confidence intervals contribute to understanding measurement error?
What does statistical power primarily indicate in research studies?
What does statistical power primarily indicate in research studies?
What is implied when experimental validity is low?
What is implied when experimental validity is low?
What does it mean for measurements to be statistically different?
What does it mean for measurements to be statistically different?
What type of error occurs when a researcher mistakenly rejects a true null hypothesis?
What type of error occurs when a researcher mistakenly rejects a true null hypothesis?
Which of the following correctly represents the probability of committing a Type I Error?
Which of the following correctly represents the probability of committing a Type I Error?
Increasing the significance level from 0.01 to 0.05 primarily affects which type of error?
Increasing the significance level from 0.01 to 0.05 primarily affects which type of error?
What is the consequence of choosing a lower significance level, such as 0.01?
What is the consequence of choosing a lower significance level, such as 0.01?
What is indicated by the term 'statistical power'?
What is indicated by the term 'statistical power'?
When a researcher fails to reject a false null hypothesis, which type of error has occurred?
When a researcher fails to reject a false null hypothesis, which type of error has occurred?
What effect does lowering the significance level have on the confidence of the results?
What effect does lowering the significance level have on the confidence of the results?
Which statement best describes a Type II Error in hypothesis testing?
Which statement best describes a Type II Error in hypothesis testing?
How does the choice of significance level influence Type I and Type II Errors?
How does the choice of significance level influence Type I and Type II Errors?
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Study Notes
Null and Alternative Hypotheses
- The null hypothesis is the starting point of a research study, assumed to be true before the experiment is conducted.
- Researchers use experimental results to determine whether there is enough evidence to reject the null hypothesis.
- If there is not enough evidence to reject it, the null hypothesis is not rejected.
Statistical Significance
- Statistical significance pertains to the probability that a null hypothesis is true, given a predetermined margin of error.
- Results that are statistically significant are unlikely to be due to chance alone.
- The p-value of a study is the probability of observing the study's results due to chance alone.
- P-values between 0 and 0.05 are considered statistically significant, while p-values greater than 0.05 are not.
Confidence Intervals
- Confidence intervals are calculated with a specified confidence level (eg. 95%).
- Confidence intervals provide a range of values within which a population parameter is likely found.
- When confidence intervals calculated for different measurements do not overlap, the results are considered statistically significant, even if the exact values differ.
- 95% confidence intervals are typically presented as sample mean ± 2 SEM.
Error Bars in Research Studies
- Error bars represent the variability of the data in a research study.
- When error bars for different measurements do not overlap, this implies a statistically significant difference between the measurements.
Experimental Validity
- Experimental validity refers to how accurately an experiment tests its intended objective and whether the results can be trusted.
- If an experiment is valid, its conclusions can be applied to real-world situations.
Types of Error in Experiments
- Type I error occurs when the null hypothesis is rejected when it should not be.
- The probability of committing a Type I error is called the alpha value (α).
- Type II error occurs when the null hypothesis is not rejected when it should be.
- The probability of committing a Type II error is called the beta value (β).
- Statistical power describes the probability that a test will detect an effect when such an effect exists, also defined as the probability of correctly rejecting the null hypothesis.
Confidence Levels
- Biological research often involves drawing conclusions from samples of a population.
- Values calculated from samples are called statistics.
- Values that describe whole populations are called parameters.
- Confidence intervals provide a range of values within which a population parameter is likely to be found.
Statistical Power
- Statistical power is the ability of a test to detect an effect when one exists.
- It is also the probability of correctly rejecting the null hypothesis.
- Power can also be described as the probability of avoiding a Type II error.
- Statistical power is increased with larger sample sizes.
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