Stats Flashcards
The more heterogenous the distribution of scores,
- The smaller the standard deviation
- The larger the standard deviation
- The more the distribution tends to be skewed
- The greater the chance of excluding extreme scores
- The larger the mean
The larger the standard deviation
The more heterogenous (different, variance, all over the place) the distribution of scores, the larger the standard deviation. Also, SD= square root of the variance, and similarly Variance = SD squared.
For any normal distribution, the 50th percentile corresponds to a z score of
- 0
- 50
- 100
- +/-1
- None of these
0
Know the normal distribution curve, the very middle is 50%.
Other things being equal, an alpha level of .05 should lead to a rejection of the null hypothesis
- More often than when alpha is set at .01
- More often than when alpha is set at .10
- Less often than when alpha is set at .01
- You cannot tell from the information provided
More often than when alpha is set at .01
Alpha is set at .05, is less strict allows for more than acceptance of findings, .01 is more strict, effect has to be larger, stronger to be accepted.
*** Rejecting Ho when in fact it should have been accepted, causes
- Standard Error
- Sampling Error
- Omega error
- Type I error
- Type II error
Type I error
Type I error: thinking you found something (rejecting the null) when in fact you havn’t found something.
*** When a difference is considered “statistically significant,” the most accurate interpretation of the finding is that the difference is probably
- Meaningful and Profound
- Due to chance and chance alone
- Not due to chance alone
- Not evident in the population
- None of these
Not due to chance alone
Statistically significant: the findings are not likely due to chance alone (because there is always chance in everything)
*** The power of a statistical test, such as the independent t-test, can be increased by
- Increasing the sample size
- Increasing the accepted alpha level (making alpha numerically larger)
- Using a one-tailed test
- All of the above
Increasing the sample size
Increasing the accepted alpha level (making alpha numerically larger)
Using a one-tailed test
ALL OF THE ABOVE
To increase power you have to do the harder stuff (get a larger sample, use one tailed instead of two tailed)
The only time the standard deviation may equal zero, is when
- The range is less than 50
- Every score in the distribution is the same
- The range is negative
- None of these, since the standard deviation may never be equal to zero
Every score in the distribution is the same
For the mean and SD to equal 1, all of the scores in the distribution have to be the same.
A scatter plat on which the array of points goes from upper left to lower right indicates
- A positive correlation
- A negative correlation
- A zero correlation
- Any of the above (a, b, or c) depending on the strength of the correlation
- None of the above (a, b, or c)
A negative correlation
Visualize, over time, things are decreasing, so negative
*** When beta is increased (becomes numerically larger)
- Power has decreased
- Power has increased
- The likelihood of rejecting the null hypothesis is maximized
- The likelihood of obtaining a significant difference is maximized
- The possibility of Type I error also is increased
Power has decreased
Power =1-B
The T score distribution always assumes a
- Mean of 50, and a SD of 10
- Mean of 100 and a SD of 15
- Mean of 100 and a SD of 10
- Mean of 0 and a SD of 1
- None of the above
Mean of 50, and a SD of 10
T score= a mean of 50, and a SD of 10 (think of assessment measures)
The mean of a given normal distribution of raw score is 200 with a standard deviation of 25. What percentage of scores is above 225?
- 5% of the scores
- 10% of the scores
- 16% of the scores
- 25% of the scores
- 34% of the scores
- There is not enough information to tell
16% of the scores
Know the normal distribution curve; percentages under SD 1 to SD 3= 16%
If the standard deviation for a particular distribution is 6, then the variance equals
- 2
- 3
- 6
- 12
- 36
36
Variance = SD squared
*** The alpha level states the probability of being wrong when
- The null hypothesis is accepted
- The null hypothesis is rejected
- The alternative hypothesis is rejected
- The sample means are assumed to be equal
- The effect size is too small to detect
The null hypothesis is rejected
Type I error (rejecting the null hypothesis) goes with alpha
Of the following correlation coefficients, which expresses the strongest association?
- .76
- .45
- .00
- -.37
- -.95
-.95
Can be positive or negative, the higher the number the better
*** Which of the following terms best indicates a process variable (X) through which one variable (A) influences another (B) (e.g., it explains how A influences B)?
- Risk Variable
- Associated Variable
- Mediator Variable
- Moderator Variable
- Correlate
Mediator Variable
Mediator variable is how/why, moderator variable is strength?
Which of the following types of data uses numbers or letters to label categories?
- Nominal
- Ordinal
- Interval
- Rank
- Ratio
Nominal
Pinot NOIR (the acronym progressively becomes more about numbers, quantitative)
A correlation coefficient of .80 yields a coefficient of determination of
- 80%
- 40%
- 64%
- 8%
- None of the above
64%
Correlation Coefficient squared= coefficient of determination
As the sample size increases, degrees of freedom
- Remain the same
- Increase
- Decrease
- Are completely unaffected
Increase
*** When the alternative hypothesis is accepted
- The “chance” explanation is completely ruled out
- The “chance” explanation, though not ruled out, is judged improbable
- The “chance” explanation is fully accepted
- The “chance” explanation becomes irrelevant
- Beta should be carefully considered as an explanation
The “chance” explanation, though not ruled out, is judged improbable
Chance is never ruled out, but seen to be improbable
Which of the following does NOT contribute to a small effect size?
- The heterogeneous sample
- A large standard deviation among the scores on the dependent variable
- Variable experimental procedures
- Use of unreliable measures
- Use of uncontrolled multiple comparisons
Use of uncontrolled multiple comparisons
When the standard deviation of the entire distribution of random sample means has been calculated, the resulting value is called
- The deviation score
- The parameter mean
- The standard error of the mean
- Sampling error
- The mean square value
The standard error of the mean
*** The smaller the effect size, the greater the likelihood
a. Of accepting the null hypothesis
b. Of rejecting the null hypothesis
c. Of committing a Type I error
d. Of committing Type II error
Both a and d
Both b and c
Both a and d
a. Of accepting the null hypothesis
d. Of committing Type II error
The mean of a given normal distribution of raw scores is 200 with a standard deviation of 25. Between the raw scores of 150 and 250, there must be
- 50% of scores
- 68% of scores
- 80% of scores
- 84% of scores
- 95% of the scores
- There is not enough information to tell
95% of the scores
Know the normal distribution curve; between SD of -2 and SD of 2, lies 95% of the scores
A researcher wishes to test the hypothesis that there is a positive association between IQ and musical ability. A random sample of 10 subjects was selected. The subjects were rank-ordered in terms of musical ability. A random sample of 10 subjects was selected. The subjects were rank-ordered in terms of musical ability. Then each subject was given an IQ test. Which of the following statistical tests is most appropriate to determine whether there is a significant relationship between IQ and musical ability in this situation?
- Spearman r
- Pearson r
- Fisher’s z
- Point-biserial correlation
- Phi coefficient
Spearman r
*** Which of the following statements is NOT true?
- The use of multiple comparisons contributes to an experiment-wise error rate that may be too high
- The experiment-wise error rate is typically higher than the alpha level set for an individual comparison
- The experiment-wise error rate is typically lower than the alpha level set for an individual comparison.
- An elevated experiment-wise error rate contributes to Type I error
- Problems with the experiment-wise error rate may cause the researcher to erroneously reject the null hypothesis
The experiment-wise error rate is typically lower than the alpha level set for an individual comparison.
When a distribution shows a large majority of high scores and a few very low scores, the distribution is said to be
- Skewed to the right
- Skewed to the left
- Skewed to the middle
- Bimodal
- None of the above
Skewed to the left
A Pearson r value of -.16
- Can never be significant
- Must always be significant
- Suggests that a slight positive relationship exists between two variables
- Cannot be evaluated for significance unless df is known
Cannot be evaluated for significance unless df is known