PSYC 210- Midterm Review
Rejecting the null hypothesis when it is false
-correct rejection -power
The mean and mode of the following set of data: 9, 2, 3, 4, 6, 8, 9, 9, 3, 3
mean = 5.6 mode = 3, 9 (both occur the most often)
A researcher records the following time (in seconds) that children in a sample play with an unfamiliar toy: 12, 14, 10, 6, 8, 10, 13, 12, 4, 12, and 6. Which measure of central tendency is largest? A. mean B. median C. mode D. all measures are equal
mean = 9.73 median = 10 mode = 12 mode has the largest value of 12
I spin one spinner with 4 sections (1, 2, 3, 4) and one with 3 sections (1, 2, 3). Event A = getting the same number on both. Event B = getting a 4. a. ME b. CE c. Both d. Neither
mutually exclusive because event B can't happen at the same time event A happens (event B has no 4)
The heights of the columns of a histogram indicate the: A. Value of the score being graphed B. Average scores C. Number of scores in a particular interval D. The midpoints of the various intervals
number of scores (frequency) in a particular interval
Assessing how happy UNC students are on a 7-point scale (1 not very happy to 7 extremely happy) would be best achieved using what type of scale? A. Nominal B. Ordinal C. Interval D. Ratio
ratio and interval
Alpha is also known as: a. significance level b. type II error c. correlation coefficient d. type 1 error
significance level and type 1 error
Why do we transform scores? A. To make life more complicated B. To make different distributions comparable C. To get a mean and standard deviation D. To change scores to raw units
to make different distributions comparable (standardization) Yes, this may make life more complicated, but the reason we transform scores is to make them comparable. Many times, we are using different metrics or distributions that don't have a one-to-one relationship. Transforming allows us to see where a score is relative to the others in its distribution.
A researcher says that her analysis shows that the result of her one-sample t-test is statistically significant. This means that the mean of her sample is: A. very likely to be predictable B. very unlikely to occur by chance C. very likely to be found again upon replication D. very unlikely to be large
very unlikely to occur by chance *accepting alternative hypothesis
If we were testing whether a particular sample was drawn from a population with a particular mean and the standard deviation of the entire population was known, we would use the _____-test to statistically answer the question.
z-test
Which of the following values of a distribution's variance is impossible? A. 2.45 B. .965 C. -.449 D. 1025
-0.449 Remember, variance is all about squaring deviations from the mean. If you square a positive or negative value, your outcome will always be positive.
slope of the line if for every two units of x, my y moves down 3
-3/2 down 3/right 2
A number is chosen at random from a standard normal distribution. What is the probability that p(z > -1.24)? A. 0.11507 B. 0.92220 C. 0.10749 D. 0.89251
0.89251 Feedback: Since we want values greater than our z-value, we need to do 1-p(z < -1.24). When we look up -1.24, we get 0.10749, do 1-0.10749=0.89251.
In a given week, there is a 50% chance it will rain on Tuesday and a 75% chance that it will rain on Wednesday (let's assume that the weather on one day does not affect the weather on the other day). What is the probability that it will rain on Tuesday AND on Wednesday? A. 37.5% B. 25% C. 100% D. 87.5%
37.5% Yup, the formula for finding the intersection of two independent events is P(A)*P(B), which calculates to .375 in this case.
My regression equation is y(hat) = 0.4x + 32 when x= 21
40.4
In a given week, there is a 50% chance it will rain on Tuesday and a 75% chance that it will rain on Wednesday (let's assume that the weather on one day does not affect the weather on the other day). What is the probability that it will rain on Tuesday OR on Wednesday? A. 87.5% B. 25% C. 100% D. 37.5%
87.5% Yup, the formula for finding the union of two independent events is P(A)+P(B)-P(A)*P(B), which calculates to .875 in this case.
The average UNC student is in the 93rd percentile of ACT scores. What does this mean?
93% of ACT takers score below the average UNC student or the average UNC student score higher than 93% of other ACT takers
A student in my class scored around a 730 on their SAT Verbal test. What percentile (to the closest tenth of a percentage) is my student at compared to everyone else who takes the SAT? As a reminder, the mean of SAT Verbal tests is 500 and the standard deviation is 100. A. 99.1% B. 98.9% C. 48.9% D. 0.9%
98.9% Feedback:Once we standardize 730, we see that our z-score is 2.30. Percentile measures how much of the population scores lower than you, so we simply have to look up 2.30 on the table and we get .98928, or approximately 98.9%.
Which of these is NOT a benefit of creating a sampling distribution? A. The standard deviation of the sampling distribution is the same as the standard deviation of the population B. The mean of the sampling distribution is the same as the mean of the population C. It keeps us from having to collect a very large sample (e.g., 10,000 people) D. It is normally distributed
A. The standard deviation of the sampling distribution is the same as the standard deviation of the population The means of the two distributions should be the same, but the standard error is not a good estimate of the standard deviation! The former changes according to sample size.
I flip one coin and roll one die. Which of these examples is collectively exhaustive? A. A = rolling a 1, B = rolling a 4 B. A = rolling less than a 4, B = rolling an odd number C. A = getting a head, B = rolling an even number D. A = rolling 2 or higher, B = rolling an odd number
A= rolling 2 or higher, B= rolling an odd number Collective exhaustivity means that the union of the two events would cover the full sample space. Regardless of the coin flip, rolling a 2 or higher and rolling an odd number covers all of the die roll options from 1-6, and since we must roll a die with every coin flip, this covers all outcomes in our sample space.
What do we mean when we use the term "variable" in statistics? A. An orderly way to assign numbers to observations B. Label that refers to some abstract concept that might differ between people C. The way we measure an abstract construct D. Coding characteristics of human behavior into discrete categories
B. label that refers to some abstract concept that might differ between people
The alternative hypothesis for an independent samples, two-tailed t-test states A. µ1 < 0 B. µ1 not equal to µ2 C. µ1 + µ2 = 0 D. µ1 > µ2
B. µ1 not equal to µ2 We want to test whether the two groups are equal to one another, therefore our alternative hypothesis is that they are not equal
What is the sample space of the experiment of flipping 2 coins and flicking a spinner with 4 sections labeled 1, 2, 3, and 4 (see attached image)? A. {H, T, 1, 2, 3, 4} B. {HH1, HH2, HH3, HH4, HT1, HT2, HT3, HT4, TT1, TT2, TT3, TT4} C. {HH1, HH2, HH3, HH4, HT1, HT2, HT3, HT4, TH1, TH2, TH3, TH4, TT1, TT2, TT3, TT4} D. {H1, H2, H3, H4, T1, T2, T3, T4}
C. {HH1, HH2, HH3, HH4, HT1, HT2, HT3, HT4, TH1, TH2, TH3, TH4, TT1, TT2, TT3, TT4} A sample space covers all possible outcomes of an experiment.
For the given scenario, choose the appropriate alternative hypothesis for my situation. My research question is: Do people who play video games regularly have better hand eye-coordination than the rest of the population? Say that we can measure hand-eye coordination as reaction time in a sports task and the population has a mean reaction time of 600 ms. A. µ > or = 600 B. µ is not equal to 600 C. µ < 600 D. µ > 600
C. µ < 600 *D. µ > 600
What is the primary purpose of learning about statistics? A. To learn the purpose of the standard normal distribution B. To show what kind of distribution you have C. So you can overwhelm your opponents with numbers D. So you can make a claim and support it with evidence
D. So you can make a claim and support it with evidence
r-squared can be interpreted as: A. the Y unit increase for a one unit increase in X. B. the slope of the line between X and Y C. the proportion of unexplained variance between X and Y D. the proportion of variance in Y that is accounted for/explained by X.
D. the proportion of variance in Y that is accounted for/explained by X.
What does P(A) * P(B) mean?
Event A and event B are independent. The probability of event A AND event B occurring.
Abbreviations for the null and alternative hypothesis _____
H0 H1
What's the sample space of flipping 2 coins and spinning a 3-part spinner (labeled 1, 2, 3)?
HH1 HH2 HH3 TH1 TH2 TH3 HT 1 HT2 HT3 TT1 TT2 TT3
How would you find the interquartile range for the data set: 12, 14, 11, 10, 9, 10, 9
IQR = 3 Median = 10 Lower quartile = 9 Upper quartile = 12 12-9 = 3
The 95% confidence interval of a value tells me this
The probability of my true population value being in that range is around 95%
Our research question informs this hypothesis ________
alternative hypothesis
You want to see how the mean height of your sample compares to the mean height of the UNC student population. Which of the following would result in a biased sample that would interfere with your research question? a. Collecting participants that went to Chase for lunch b. Collecting participants from the basketball team c. Collecting participants from a Chem 101 class
b. collecting participants from the basketball team Explanation: biased sample would be participants from the basketball team; they are usually taller so the sampling representation is leaning towards taller, athletes
The preferred graph to display the frequency of variables measured on a nominal scale is a: A. Pie chart B. Bar chart C. Boxplot D. Histogram
bar chart
Why are measures of variability so important? A. Because standard deviations are not as interpretable as variance B. Because it tells us how representative our measures of central tendency are C. Because measures of central tendency are so flawed D. Because statistics are used only to report variance
because it tells us how representative our measures of central tendency are Feedback:Measures of central tendency are great and they give us valuable information about what the average or typical response is. However, you need an add-on to tell you just how representative your measure of central tendency is. If you have a lot of variability, that indicator of central tendency isn't quite as good at describing the whole distribution.
what effects does confidence level have on margin error?
confidence level increases, margin of error increases explanation: large intervals of the expected proportion causes a larger room for error
r is called the a. correlation coefficient b. effect size c. standard error d. variance
correlation coefficient
type of variable (discrete/continuous) and choice from (NOIR) of number of petals on a flower
discrete (can't have 0.3 petals on flower) ratio (true zero exists and differences between measurements make sense)
In hypothesis testing, a Type II Error is: A. failing to reject the null hypothesis when the null hypothesis is false B. rejecting the null hypothesis when the null hypothesis is false C. failing to reject the null hypothesis when the null hypothesis is true D. rejecting the null hypothesis when the null hypothesis is true
failing to reject the null hypothesis when the null hypothesis is false -accepting null when null is false
In a frequency distribution graph, frequencies are presented on the __________ and the scores (categories) are listed on the __________. A. class interval; horizontal line B. horizontal line; vertical line C. X axis; Y axis D. Y axis; X axis
frequencies presented on the y- axis scores (categories) listed on x-axis
What effects does sample size and sample standard deviation have on margin of error?
increase sample size, decrease margin of error increase sampling deviation, increase margin of error
What type of test is most appropriate when you want to compare an outcome between two groups with different people in each group? A. Z-test B. Paired t-test C. Independent samples t-test D. t-test for correlations
independent samples t-test *unpaired t-test
Why is the mean the most commonly used measure of central tendency? A. The median and the mode don't tell us much when variables are discrete B. It isn't the most commonly used C. The mean is more appropriately used to describe variability D. It gives us one, simple number that takes all data into account
it gives us one, simpler number that takes all data into account -even if it is easily influenced by outliers, it can account for all data
when comparing confidence interval's of different groups, the distribution graph don't overlap. What can we conclude?
it is inconclusive
Which graph would you use to look analyze profits of Apple products are being sold over the past 10 years?
line graph/plot
The shape of our sampling distribution after we take 50,000 samples from a negatively skewed population _______
normally distributed
Using letter grades (A, B, C, D, and F) to classify student performance is an example of measurement on a(n) __________ scale of measurement. A. Ordinal B. Ratio C. Interval D. Nominal
ordinal
What type of test is most appropriate when you want to compare an outcome between the same people at two different time points? A. Paired t-test B. Independent samples t-test C. t-test for correlations D. Correlations and regressions
paired t-test
A researcher is curious about the average IQ of all currently registered voters in the state of Iowa. If this average could be obtained, it would be an example of a __________. A. sample B. population C. parameter D. statistic
parameter Feedback:If all we care about are the registered voters in Iowa and we CAN get access to their data, we have accessed the population of interest. Once we calculate an average, we have a parameter.
Kurtosis refers to: A. the average distance of scores from a measure of central tendency B. the peakedness or flatness of a distribution C. whether the distribution is continuous or discrete D. whether more extreme scores are in one tail of the distribution as opposed to other
peakedness or flatness of a distribution "kurtosis" greek for convex (shape of distribution)
A correlation coefficient of -1.0 indicates: A. a perfect negative relationship between the two variables. B. a perfect positive relationship between the two variables. C. no relationship between the two variables. D. nothing
perfectly negative relationship b/n the two variables -strong, negative
The name for a graph that is very pointy? very flat?
pointy graph - leptokurtic (more data centered in the middle) flat graph - platykurtic (little data in the middle, more on the tails)
he process of selecting a subset of people from a population such that every person in the population has an equal chance of being chosen for the subset is called: A. population sampling B. standardizing the population C. stimulus sampling D. random sampling
random sampling the concept in which everyone (or each sample) has an equal chance of being selected. Gathers a more representative population sample that is generalizable to the population
A researcher measures scores in two groups (n = 12 in each group ) with M1 = 9.8 and M2 = 4.8. In this study, the estimated standard error for difference scores is 2.0. What is the decision for a related samples t-test using a two-tailed test at a .05 level of significance?
reject the null
In hypothesis testing, a Type I Error is: A. rejecting the null hypothesis when the null hypothesis is false B. rejecting the null hypothesis when the null hypothesis is true C. failing to reject the null hypothesis when the null hypothesis is true D. failing to reject the null hypothesis when the null hypotheses is false
rejecting the null when the null hypothesis is true -accepting alternative when the null is true
The Central Limit Theorem tells us that the shape of a sampling distribution of means approaches a normal distribution as: A. the standard deviation of the population approaches the standard error of the mean. B. the mean of the population distribution gets larger. C. the sample size gets larger. D. the sample distribution skews positively or negatively.
sample size gets larger Feedback:The more samples we take, the greater the likelihood that our distribution will become more normal. More information is better than less information.
In multiple regression, we can use standardized regression coefficients (betas) to: A. see which predictor is the strongest/best predictor. B. see which outcome is the best. C. give us an indicator of relationship strength. D. Both A and C
see which predictor is the strongest/best predictor C. gives us an indicator of relationship strength
What inferential test is used to determine the significance of the relation between X and Y? A. Z test B. t-test C. F test D. Chi-square
t-test
The p-value of our result is a measure of: A. the probability of finding a sample with a mean as or more extreme than ours B. the critical value that we compare our test statistic against C. the probability of getting a sample with our mean D. our Type I error rate
the probability of finding a sample with a mean as or more extreme than ours
Which of these is NOT an assumption we make before we run an independent samples t-test? A. Independence B. Homogeneity of variance (homoscedasticity) C. Outcomes are normally distributed D. The two samples are the same size
the sample sizes are the same Feedback: We don't need sample size to be the same when using independent samples since we account for the sample size of each in the equation for the t-score. We do care about this in paired samples t-tests, though!
What is the purpose of a bar chart, histogram, or line chart? A. To see what your distribution looks like before performing any statistical tests B. To visually display otherwise confusing numbers C. To utilize current technology to present relevant statistics D. Both A and B
to see what your distribution looks like before performing any statistical tests to visually display otherwise confusing numbers
A pregnancy test says that the person is not pregnant when they are. This is called _________
type 2 error -rejecting the alternative but the alternative is true -failing to reject the null, when the null is false
If the confidence interval contains the H null value, then what do we conclude?
we fail to reject the null we accept the null Explanation: the null is part of our probability of getting the sample value, so there's no significant difference.
A correlation coefficient of -1.32 tells us: A. there is a very strong negative relationship between x and y. B. there is a very strong positive relationship between x and y. C. the relationship between x and y is not linear. D. we have calculated the correlation coefficient incorrectly.
we have calculated the correlation coefficient incorrectly because it can only be within -1 to 1
When would the median be the most effective way to describe a distribution? A. When data are categorical with few categories B. When data are continuous and the distribution is skewed by outliers C. When the mean, median, and mode are the same and you have a continuous variable D. When the mean, median, and mode are the same and you have a categorical variable
when data are continuous and distribution is skewed by outliers