Data Analysis Oral Exam
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PSYB07 - Theory Questions stats 3 PSYB07 NEW psyc08
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1. Explain the relationship between Type I errors, Type II A type 1 error is a false positive and occurs when a researcher incorrectly rejects a
errors, confidence level and power. How do you true null hypothesis.
calculate the probability of occurrence for each of these A type II error is when one accepts a null hypothesis that is actually false.
elements in an experiment? 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 Power analysis will tell you the probability of avoiding a Type II error.
ways in which power can be increased 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 The p value will directly correlate to the hypothesis. A smaller p-value means that
sizes? Do sample sizes affect p-values? What about there is stronger evidence in favor of the alternative hypothesis.
effect sizes? 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 The dependent variable must be continuous (interval/ratio).
are these assumptions necessary? How would violations The observations are independent of one another.
to these assumptions affect the interpretation of the The dependent variable should be approximately normally distributed.
results of a t-test? 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 A sample size that is too small reduces the power, outliers skew the results.
assumptions? What about outliers?
Save
Students also studied
PSYB07 - Theory Questions stats 3 PSYB07 NEW psyc08
13 terms 19 terms 20 terms 19 terms
Isabelle1313131 Preview virginiageissinger Preview quizlette5388453 Preview ajom
1. Explain the relationship between Type I errors, Type II A type 1 error is a false positive and occurs when a researcher incorrectly rejects a
errors, confidence level and power. How do you true null hypothesis.
calculate the probability of occurrence for each of these A type II error is when one accepts a null hypothesis that is actually false.
elements in an experiment? 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 Power analysis will tell you the probability of avoiding a Type II error.
ways in which power can be increased 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 The p value will directly correlate to the hypothesis. A smaller p-value means that
sizes? Do sample sizes affect p-values? What about there is stronger evidence in favor of the alternative hypothesis.
effect sizes? 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 The dependent variable must be continuous (interval/ratio).
are these assumptions necessary? How would violations The observations are independent of one another.
to these assumptions affect the interpretation of the The dependent variable should be approximately normally distributed.
results of a t-test? 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 A sample size that is too small reduces the power, outliers skew the results.
assumptions? What about outliers?
