Statistics Final
A population has = 60 and = 30. For a sample of n = 25 scores from this population, a sample mean of M = 55 would be considered an extreme value.
False
A researcher would like to compare two treatment conditions with a set of 30 scores in each treatment. If a repeated-measures design is used, the study will require n = 60 participants.
False
A sample of n = 25 scores is selected from a population with a variance of 2 = 16. The sample variance probably will be smaller than 16.
False
A score equal to the 5th percentile is one of the highest scores in the distribution.
False
A two-factor study compares 2 levels of factor A and 3 levels of factor B with a sample of n = 5 participants in each treatment condition. This study will use a total of 25 participants.
False
After a researcher adds 5 points to every score in a sample, the standard deviation is found to be s = 10. The original sample had a standard deviation of s = 5.
False
Classifying people into two groups on the basis of gender is an example of measurement on an ordinal scale.
False
For any distribution of scores, at least one individual will have a score exactly equal to the mean.
False
For any normal distribution, the proportion located between the mean and z = 1.40 is 0.9192.
False
For the following scores, (X - 1) = 10. Scores: 1, 3, 7
False
If other factors are held constant, the larger the values for the two sample variances, the greater the likelihood that the independent-measures t test will find a significant difference.
False
If two sample variances are not equal, the pooled variance will be closer to the larger of the two variances.
False
In a distribution with µ = 80 and = 20, a score of X = 95 corresponds to z = 1.50.
False
In a hypothesis test, a large value for the sample variance increases the likelihood that you will find a significant treatment effect.
False
In an analysis of variance, MStotal = MSbetween + MSwithin.
False
In an analysis of variance, if all other factors are held constant, the larger the sample variances, the bigger the value for the F-ratio.
False
In general, the larger the value of the sample variance, the greater the likelihood of rejecting the null hypothesis
False
Obtaining a significant interaction means that both factors A and B have significant main effects.
False
The average score for a population is an example of a statistic.
False
The null hypothesis for the independent-measures t test states that there is no difference between the two sample means.
False
The null hypothesis states that the sample mean (after treatment) is equal to the original population mean (before treatment).
False
The scores for a very easy exam would probably form a positively skewed distribution.
False
A Type I error occurs when a treatment has no effect but the decision is to reject the null hypothesis.
True
A sample has a mean of M = 40. If a new score of X = 55 is added to the sample, then the sample mean would increase.
True
A sample of n = 6 scores has X = 48. This sample has a mean of M = 8.
True
A score with a value less than or equal to the mean will have a z-score that is less than or equal to zero.
True
A significant treatment effect does not necessarily indicate a large treatment effect.
True
A two-factor ANOVA consists of three separate hypothesis tests.
True
A two-factor study compares three different treatment conditions (factor 1) for males and females (factor 2). In this study, the main effect for gender is determined by the overall mean score for the males (averaged over the three treatments) and the corresponding overall mean score for the females.
True
A vertical line drawn through a normal distribution at z = -0.80 will separate the distribution into two sections. The proportion in the larger section is 7881.
True
According to the central limit theorem, the standard error for a sample mean becomes smaller as the sample size increases.
True
As sample size increases, the critical region boundaries for a two-tailed test with = .05 will move closer to zero.
True
Because the repeated-measures ANOVA removes variance caused by individual differences, it usually is more likely to detect a treatment effect than the independent-measures ANOVA is.
True
Changing the value of a score in a distribution will always change the value of the mean.
True
F-ratios are always greater than or equal to zero.
True
For ANOVA, when the null hypothesis is true, the F-ratio is balanced so that the numerator and the denominator are both measuring the same sources of variance.
True
For a normal distribution, the proportion in the tail beyond z = 1.50 is p = 0.0668
True
For a population with a mean of = 80, any score greater than 80 will have a positive z-score.
True
For a population with a standard deviation of = 12, a z-score of z = +0.50 corresponds to a score that is above the mean by 6 points.
True
For a positive correlation, decreases in X tend to be accompanied by decreases in Y
True
For a repeated-measures study, a small variance for the difference scores indicates that the treatment effect is consistent across participants.
True
For the following distribution of scores, sum of X = 18. X f 4 1 3 2 2 3 1 2
True
If a negatively skewed distribution has a mean of 50, then the median and the mode are both probably greater than 50.
True
If a researcher expects that a new teaching technique will be more effective for children who are more than 10 years old than it is for younger children, then the researcher is predicting an interaction between the teaching technique and age.
True
If other factors are held constant, as the sample size increases, the estimated standard error decreases.
True
If other factors are held constant, then increasing the sample size will increase the likelihood of rejecting the null hypothesis.
True
If samples of size n = 16 are selected from a population with = 40 and = 8, the distribution of sample means will have a standard error of 2 points.
True
If the research prediction is that the treatment will decrease scores, then the critical region for a directional test will be in the left-hand tail.
True
If the sample data are in the critical region with = .01, then the same sample data would still be in the critical region if were changed to .05.
True
If the standard deviation for a population increases, the standard error for sample means from the population will also increase.
True
If you have a score of X = 66 on an exam with = 70 you should expect a better grade if = 10 than if = 5
True
In a repeated-measures ANOVA, individual differences are measured by SSbetween subjects.
True
In the second stage of analysis for the repeated-measures ANOVA, individual differences are removed from the denominator of the F-ratio.
True
Post-Hoc tests are only needed if H0 is rejected in an ANOVA comparing more than 2 treatments.
True
Repeated-measures designs are particularly well-suited to research studies examining learning or other changes that occur over time.
True
The homogeneity of variance assumption states that the two population variances are equal.
True
The larger the differences among the sample means, the larger the numerator of the F-ratio will be.
True
The power of a hypothesis test is the probability that the sample mean will be in the critical region if the treatment has an effect.
True
The repeated-measures ANOVA begins the same way as the independent-measures ANOVA, with the total variability partitioned into between-treatment and within-treatment components.
True
The sample mean will always be exactly in the center of a confidence interval that is estimating the value of the population mean.
True
The t distribution for df = 4 is flatter and more spread out than the t distribution for df = 20
True
The value obtained for Cohen's d is independent of the sample size.
True
There is always a possibility that the decision reached in a hypothesis test is incorrect.
True
Two samples from same population probably will have different t statistics even if they are the same size and have the same mean.
True
Whenever a two-factor experiment results in a significant interaction, you should be cautious about interpreting the main effects because an interaction can distort, conceal, or exaggerate the main effects of the individual factors.
True
