QUIZ
ANSWERS
You've got the idea!
1.
- a statistic NO
- a histogram YES
- a scatter plot YES
- a stem and leaf plot YES
2.
- for simpletons NO
- presented in stem and leaf plots NO
- things like correlations NO
- things like standard deviations YES
3.
- Use it do show spread. NO
- Use it for normally distributed data. NO NOT USUALLY
- Cross it against oncoming traffic. CAREFULLY!
- Indicate the middle of some data. YES
4.
- standard deviation YES
- root mean square errors YES
- percentile ranges YES
- polyunsaturated margarine SORT OF.
5.
6.
- a risk of matrimony NO
- an outcome statistic YES
- a relative of the odds ratio YES
- a relative frequency YES
7.
- p values NO
- standard errors of the mean DEFINITELY NOT!
- percentages of the mean NO
- standard deviations YES
8.
- describes loss of precision. NO
- describes factor analysis. YES
- is an example of ANOVA. NO
- is a weight-loss program. NO, BUT...
9.
- It impacts most on descriptive studies. NO
- It can be expressed as an ICC. YES
- It can be expressed as a CV. YES
- It is quantified by 2-way ANOVA. YES
10.
- It impacts most on descriptive studies. YES
- It is the correlation between true and observed values. YES
- A valid measure must be reliable. YES
- A reliable measure must be valid. NO
11.
- height = 175 ± 6 cm CORRECT
- VO2peak = 67 ± 5.1 ml/min/kg INCORRECT
- ICC = 0.87 CORRECT
- CV = 1.4% CORRECT
12.
- are a new form of sprint training. NOT A BAD IDEA...
- are calculated routinely by most stats packages. NO, CURSE
THEM
- define the likely range of a population value. YES
- are inferior to p values for indicating magnitude of outcomes.
OF COURSE NOT
13.
- The correlation is significant. NO
- The true value of the correlation is likely to be 0.45. NO
- More subjects should be tested. YES
- A type II error has occurred. NO (the population value might
be insubstantial)
14.
- One-tailed tests are sometimes justified. NOT IN MY BOOK
- Test statistics should always be shown. NO POINT
- Chi-squared is a common test statistic. YES
- P = 0.06 means there is no effect. ABSOLUTELY WRONG
15.
- hardly ever NO
- about one time in 100 NO
- about one time in 20 NO (you'd get this if the population
correlation was zero, not 0.70)
- almost always. YES (the only possible correct answer)
16.
- Differences between all groups are substantial. YES
- The data should be analyzed by repeated-measures ANOVA. NO
- Log transformation appears to be necessary before analysis. NO
(rank transform, yes)
- Runners are lazier than cyclists. NO (running is harder than
cycling)
17.
- is an example of an ordinal variable. YES
- has a behavior problem when it comes to residuals. YES
- should be analyzed by logistic regression. YES
- can be analyzed by ANOVA. NO
18.
- if the values are too big. NO
- if the residuals (error) get bigger for bigger values of the
variable. YES
- if you don't get statistical significance. NO
- if non-parametric tests are inappropriate. NO
19.
- are parametric tests in disguise. YES
- involve rank transformation of the dependent variable. YES
- work for grossly non-normal data. YES
- should be attempted if parametric tests give p > 0.05. NO
(but that's what some people do!)
20.
- unpaired t test. NO
- ANCOVA. YES
- ANOVA. NO
- MANOVA. NO
21.
- Use it to fit curves as well as straight lines. YES
(polynomials)
- Use it to control for the effect of numeric variables. YES
- It gives misleading results for highly correlated independent
variables. YES (neither appear to contribute in the presence of
the other)
- Use it to fit multiple straight lines with several groups. NO
22.
- are used in descriptive studies. NO
- can be analyzed by modeling variances. YES
- are used when you have to repeat a failed test. NO
- are straightforward to analyze with stats programs. NO!
23.
- Initial randomization to the two groups was poor. NO. (How can
you randomize sex!?)
- There is one between- and one within-subject factor. YES
- The time effect in the model is substantial. NO (time*sex is)
- The time effect in the model is significant. NO (need sample
sizes to tell)
24.
- Sample size is proportional to (1 - r), where r=reliability
correlation. YES
- Controlled studies need 4x as many subjects as uncontrolled
studies. YES
- Get sample size "on the fly" by testing until you get an
acceptable confidence interval. YES
- None of the above. NO
25.
- depends on the size of your research grant. NO/YES
- is inversely proportional to the square of the validities of
your measures. YES
- is a function of the largest effect you want to detect. NO
(the smallest...)
- depends on how many student researchers you have on the
project. NO/YES
26.
- from now on you will show as few numbers as possible. YES
- statistical modeling is no substitute for knowing your data.
YES
- it's important to play with stats programs. YES
- from now on you will test rather that estimate. NO!
resources=AT=sportsci.org · webmaster=AT=sportsci.org · Sportsci Homepage · Copyright
©1997
Last updated 25 April 97