Discovering Statistics SPSS Chapter 2
Exam Questions and Answers
If the p<.05 we can only say we reject the null. We can't say the null is false. and we
can't say the alternative hyp is true. - ANSWER-And if p> .05 we say we failed to reject
the null hyp. We don't say the null is true and we can't say we have proved the
alternative hyp is false.
α-level - ANSWER-the probability of making a Type I error (usually this value is .05 or
5% of the time). so 5% of the time we could publish our positive findings but then find
the sample wasn't representative of the population. If we change the value to p<.05 we
reduce the likelihood of error
Alternative hypothesis - ANSWER-the prediction that there will be an effect (i.e., that
your experimental manipulation will have some effect or that certain variables will relate
to each other).
β-level - ANSWER-the probability of making a Type II error (Cohen, 1992, suggests a
maximum value of .2).
Bonferroni correction - ANSWER-a correction applied to the α-level to control the overall
Type I error rate when multiple significance tests are carried out. Each test conducted
should use a criterion of significance of the α-level (normally .05) divided by the number
of tests conducted. This is a simple but effective correction, but tends to be too strict
when lots of tests are performed.
Central limit theorem - ANSWER-this theorem states that when samples are large
(above about 30) the sampling distribution will take the shape of a normal distribution
regardless of the shape of the population from which the sample was drawn. For small
samples the t-distribution better approximates the shape of the sampling distribution.
We also know from this theorem that the standard deviation of the sampling distribution
(i.e., the standard error of the sample mean) will be equal to the standard deviation of
the sample(s) divided by the square root of the sample size (N).
Cohen's d - ANSWER-it is the differencce between two means divided by the standard
deviation of the mean of the control group, or a pooled estimate based on the standard
deviations of both groups.
An effect size that expressed the difference between two means in standard deviation
units. In general it can be estimated using the formula above.
, Confidence interval - ANSWER-A CI for the mean is a range of scores, constructed so
that the population mean will fall within this range in 95% of samples. THE CI is not an
interval within which we are 95% confident that the population mean will fall.
Degrees of freedom - ANSWER-an impossible thing to define in a few pages, let alone
a few lines. Essentially it is the number of 'entities' that are free to vary when estimating
some kind of statistical parameter. In a more practical sense, it has a bearing on
significance tests for many commonly used test statistics (such as the F-ratio, t-test, chi-
square statistic) and determines the exact form of the probability distribution for these
test statistics. The explanation involving soccer players in Chapter 2 is far more
interesting...
Deviance - ANSWER-the difference between the observed value of a variable and the
value of that variable predicted by a statistical model.
Effect size - ANSWER-The effect size is a way of measuring the size of an observed
effect, usually relative to the background error. It's an objective and (usually)
standardized measure of the magnitude of an observed effect. Measures include
Cohen's d, Glass's g and Pearson's correlations coefficient, r.
Experimental hypothesis - ANSWER-synonym for alternative hypothesis.
Experimentwise error rate - ANSWER-the probability of making a Type I error in an
experiment involving one or more statistical comparisons when the null hypothesis is
true in each case.
Familywise error rate - ANSWER-the probability of making a Type I error in any family
of tests when the null hypothesis is true in each case. The 'family of tests' can be
loosely defined as a set of tests conducted on the same data set and addressing the
same empirical question.
Fit - ANSWER-how sexually attractive you find a statistical test. Alternatively, it's the
degree to which a statistical model is an accurate representation of some observed
data. (Incidentally, it's just plain wrong to find statistical tests sexually attractive.)
Linear model - ANSWER-a model that is based upon a straight line.
Meta-analysis - ANSWER-this is a statistical procedure for assimilating research
findings. It is based on the simple idea that we can take effect sizes from individual
studies that research the same question, quantify the observed effect in a standard way
(using effect sizes) and then combine these effects to get a more accurate idea of the
true effect in the population.
Method of least squares - ANSWER-a method of estimating parameters (such as the
mean, or a regression coefficient) that is based on minimizing the sum of squared
Exam Questions and Answers
If the p<.05 we can only say we reject the null. We can't say the null is false. and we
can't say the alternative hyp is true. - ANSWER-And if p> .05 we say we failed to reject
the null hyp. We don't say the null is true and we can't say we have proved the
alternative hyp is false.
α-level - ANSWER-the probability of making a Type I error (usually this value is .05 or
5% of the time). so 5% of the time we could publish our positive findings but then find
the sample wasn't representative of the population. If we change the value to p<.05 we
reduce the likelihood of error
Alternative hypothesis - ANSWER-the prediction that there will be an effect (i.e., that
your experimental manipulation will have some effect or that certain variables will relate
to each other).
β-level - ANSWER-the probability of making a Type II error (Cohen, 1992, suggests a
maximum value of .2).
Bonferroni correction - ANSWER-a correction applied to the α-level to control the overall
Type I error rate when multiple significance tests are carried out. Each test conducted
should use a criterion of significance of the α-level (normally .05) divided by the number
of tests conducted. This is a simple but effective correction, but tends to be too strict
when lots of tests are performed.
Central limit theorem - ANSWER-this theorem states that when samples are large
(above about 30) the sampling distribution will take the shape of a normal distribution
regardless of the shape of the population from which the sample was drawn. For small
samples the t-distribution better approximates the shape of the sampling distribution.
We also know from this theorem that the standard deviation of the sampling distribution
(i.e., the standard error of the sample mean) will be equal to the standard deviation of
the sample(s) divided by the square root of the sample size (N).
Cohen's d - ANSWER-it is the differencce between two means divided by the standard
deviation of the mean of the control group, or a pooled estimate based on the standard
deviations of both groups.
An effect size that expressed the difference between two means in standard deviation
units. In general it can be estimated using the formula above.
, Confidence interval - ANSWER-A CI for the mean is a range of scores, constructed so
that the population mean will fall within this range in 95% of samples. THE CI is not an
interval within which we are 95% confident that the population mean will fall.
Degrees of freedom - ANSWER-an impossible thing to define in a few pages, let alone
a few lines. Essentially it is the number of 'entities' that are free to vary when estimating
some kind of statistical parameter. In a more practical sense, it has a bearing on
significance tests for many commonly used test statistics (such as the F-ratio, t-test, chi-
square statistic) and determines the exact form of the probability distribution for these
test statistics. The explanation involving soccer players in Chapter 2 is far more
interesting...
Deviance - ANSWER-the difference between the observed value of a variable and the
value of that variable predicted by a statistical model.
Effect size - ANSWER-The effect size is a way of measuring the size of an observed
effect, usually relative to the background error. It's an objective and (usually)
standardized measure of the magnitude of an observed effect. Measures include
Cohen's d, Glass's g and Pearson's correlations coefficient, r.
Experimental hypothesis - ANSWER-synonym for alternative hypothesis.
Experimentwise error rate - ANSWER-the probability of making a Type I error in an
experiment involving one or more statistical comparisons when the null hypothesis is
true in each case.
Familywise error rate - ANSWER-the probability of making a Type I error in any family
of tests when the null hypothesis is true in each case. The 'family of tests' can be
loosely defined as a set of tests conducted on the same data set and addressing the
same empirical question.
Fit - ANSWER-how sexually attractive you find a statistical test. Alternatively, it's the
degree to which a statistical model is an accurate representation of some observed
data. (Incidentally, it's just plain wrong to find statistical tests sexually attractive.)
Linear model - ANSWER-a model that is based upon a straight line.
Meta-analysis - ANSWER-this is a statistical procedure for assimilating research
findings. It is based on the simple idea that we can take effect sizes from individual
studies that research the same question, quantify the observed effect in a standard way
(using effect sizes) and then combine these effects to get a more accurate idea of the
true effect in the population.
Method of least squares - ANSWER-a method of estimating parameters (such as the
mean, or a regression coefficient) that is based on minimizing the sum of squared
