Data Analysis Oral Exam questionswith 100% correct answers
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Course
CCHI ORAL
Institution
Oxford University (OX)
1. Explain the relationship between Type I errors, Type II errors, confidence level and power. How do you calculate the probability of occurrence for each of these elements in an experiment?
A type 1 error is a false positive and occurs when a researcher incorrectly rejects a true null hypothesis....
Data Analysis Oral Exam questionswith
100% correct answers
1. Explain the relationship between Type I errors, Type II errors, confidence level and power.
How do you calculate the probability of occurrence for each of these elements in an
experiment? - answer A type 1 error is a false positive and occurs when a researcher incorrectly
rejects a true null hypothesis.
A type II error is when one accepts a null hypothesis that is actually false.
The probability that the value of a parameter falls within a specified range of values.
Power is the probability of avoiding a Type II error
2. What is the purpose of a power analysis? Explain the ways in which power can be increased -
answer Power analysis will tell you the probability of avoiding a Type II error.
Using a larger sample is often the most practical way to increase power, using a higher
significance level increases the probability that you reject the null hypothesis.
3. What is a p-value? How do p-values differ from effect sizes? Do sample sizes affect p-values?
What about effect sizes? - answer The p value will directly correlate to the hypothesis. A smaller
p-value means that there is stronger evidence in favor of the alternative hypothesis.
A significant p-value tells us that an intervention works, whereas an effect size tells us how
much it works. In other words, which hypothesis is supported and by how much.
Larger the sample size, smaller is the p-values if the null hypothesis is false.
If your effect size is small then you will need a large sample size in order to detect the difference
otherwise the effect will be masked by the randomness in your samples.
4. What are the assumptions for a one-sample t-test? Why are these assumptions necessary?
How would violations to these assumptions affect the interpretation of the results of a t-test? -
answer The dependent variable must be continuous (interval/ratio).
The observations are independent of one another.
The dependent variable should be approximately normally distributed.
, The dependent variable should not contain any outliers.
Assumptions are necessary or else the test won't be effective.
The t-test won't be valid if one of the assumptions is violated.
5. How would small sample sizes affect a t-test and its assumptions? What about outliers? -
answer A sample size that is too small reduces the power, outliers skew the results.
6. How are a one-sample t-test and a paired-sample t-test similar? - answer The test statistic for
the Paired Samples t Test follows the same formula as the one sample t test.
7. Explain how an outlier in a set of raw scores might affect the results of a non-parametric test.
- answer Outliers reduce the probability of Type I errors and increase the probability of Type II
errors, so that power declines.
8. Explain the difference between parametric and non-parametric tests. When is it appropriate
versus inappropriate to use each type of test, and why? - answer Parametric tests make
assumptions about the parameters of the population distribution from a sample.
Nonparametric statistics are not based on assumptions. The data can be collected from a
sample that does not follow a specific distribution.
9. Explain how overgeneralization can affect your predicted values in a regression. - answer
Overgeneralization can skew the results and make them less accurate.
10. Why do we need to test for linearity when conducting a correlation and regression analysis?
- answer Regression and correlation needs the relationship between the independent and
dependent variables to be linear. Or else the test is not effective.
11. What are residuals in a regression analysis? - answer A residual is the vertical distance
between a data point and the regression line
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