DATA ANALYSIS ORAL EXAM
QUESTIONS WITH VERIFIED AND
CORRECT ANSWERS GRADED A+
2026 UPDATE.
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.
QUESTIONS WITH VERIFIED AND
CORRECT ANSWERS GRADED A+
2026 UPDATE.
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.
