Chapter 4 and 5

Question 1

What three measures does central tendency refer to?

Answer 1

Central tendency refers to three slightly different ways to describe what is happening in the center of a distribution of data: the mean, the median, and the mode.

Question 2

Explain the term “Central Tendency”?

Answer 2

Central tendency refers to the descriptive statistic that best represents the center of a data set, the particular value that all the other data seem to be gathering around.

Question 3

Explain what the mean of a dataset is?

Answer 3

The mean is the arithmetic average of a group of scores. It is calculated by summing all the scores in a data set and then dividing this sum by the total number of scores.

Question 4

What is a Statistic?

Answer 4

A statistic is a number based on a sample taken from a population; statistics are usually symbolized by Latin letters.

Question 5

What is a Parameter?

Answer 5

A parameter is a number based on the whole population; parameters are usually symbolized by Greek letters.

Question 6

What is the formula for calculating the Mean of a set of scores?

Answer 6

The formula for the mean is: M= ∑X ÷ N To calculate the mean, we add up every score, and then divide by the total number of scores.

Question 7

Explain what is the Median of a set of scores?

Answer 7

The median is the middle score of all the scores in a sample when the scores are arranged in ascending order. If there is no single middle score, the median is the mean of the two middle scores.

Question 8

What is the Mode of a set of scores.

Answer 8

The mode is the most common score of all the scores in a sample.

Question 9

What is the most common indicator of central tendency?

Answer 9

The mean is the most common indicator of central tendency, but it is not always the best. When there is an outlier or few observations, it is usually better to use the median.

Question 10

What does the Central Tendency describe?

Answer 10

The central tendency of a distribution is the one number that best describes what is typical in that distribution (often its high point).

Question 11

What are the three measures of Central Tendency?

Answer 11

The three measures of central tendency are the mean (arithmetic average), the median (middle score), and the mode (most frequently occurring score).

Question 12

What is the most common measure of central tendency?

Answer 12

The mean is the most commonly used measure of central tendency, but the median is preferred when the distribution is skewed.

Question 13

What meanings do symbols convey in statistics?

Answer 13

The symbols used in statistics have very specific meanings; changing a symbol even slightly can change its meaning a great deal.

Question 14

Variability is indicated by?

Answer 14

4.3: Variability is the second most common concept (after central tendency) to help us understand the shape of a distribution. Common indicators of variability are range, variance, and standard deviation.

Question 15

What is variability?

Answer 15

Variability is a numerical way of describing how much spread there is in a distribution.

Question 16

What is Range?

Answer 16

The range is a measure of variability calculated by subtracting the lowest score (the minimum) from the highest score (the maximum).The formula for the range is: range = Xhighest – Xlowest, We simply subtract the lowest score from the highest score to calculate the range.

Question 17

Want is Variance

Answer 17

Variance is the average of the squared deviations from the mean.

Question 18

What is the formula for Variance

Answer 18

The sum of squares, symbolized as SS, is the sum of each score’s squared deviation from the mean. The formula for variance is: SD2= √(X-M)² / N: To calculate variance, subtract the mean (M) from every score (X) to calculate deviations from the mean; then square these deviations, sum them, and divide by the sample size (N). By summing the squared deviations and dividing by sample size, we are taking their mean.

Question 19

What is standard deviation?

Answer 19

The standard deviation is the square root of the average of the squared deviations from the mean; it is the typical amount that each score varies, or deviates, from the mean. The most basic formula for standard deviation is: SD = √SD2². We simply take the square root of the variance.

Question 20

How do we calculate the Standard Deviation

Answer 20

The full formula for standard deviation is: SD= √(X-M)² / N. To determine standard deviation, subtract the mean from every score to calculate deviations from the mean. Then, square the deviations from the mean. Sum the squared deviations, then divide by the sample size. Finally, take the square root of the mean of the squared deviations.

Question 21

What is the Interquartile Range?

Answer 21

The interquartile range is a measure of the distance between the first and third quartiles.

Question 22

What does the first quartile and third quartile represent?

Answer 22

The first quartile marks the 25th percentile of a data set. The third quartile marks the 75th percentile of a data set.

Question 23

Define the interquartile range?

Answer 23

The interquartile range (IQR) is the difference between the first quartile (Q1), the median of the lower half of the scores, and the third quartile (Q3), the median of the upper half of the scores. The formula is: IQR 5 Q3 2 Q1.

Question 24

What is the interquartile range?

Answer 24

The interquartile range is the distance from the 25th percentile (first quartile) to the 75th percentile (third quartile). It is often a better measure of variability than the range because it is not affected by outliers.

Question 25

What is the simplest way to measure variability.

Answer 25

The simplest way to measure variability is by using the range, which is calculated by subtracting the lowest score from the highest score.

Question 26

What does variation and standard deviation measure?

Answer 26

Variance and standard deviation both measure the degree to which scores in a distribution vary from the mean. The standard deviation is simply the square root of the variance: It represents the typical deviation of a score from the mean.

Question 27

How do you calculate the interquartile range?

Answer 27

The interquartile range is calculated by subtracting the score at the 25th percentile from the score at the 75th percentile. It communicates the width of the middle 50% of the data.

Question 28

What is a random sample?

Answer 28

A random sample is one in which every member of the population has an equal chance of being selected into the study.

Question 29

What is a convenience sample?

Answer 29

A convenience sample is one that uses participants who are readily available. There are two main types of samples in social science research. In the ideal type (a random sample), every member of the population has an equal chance of being selected to participate in a study. In the less ideal but more common type (a convenience sample), researchers use participants who are readily available.

Question 30

What does generalizability refer to?

Answer 30

Generalizability refers to researchers’ ability to apply findings from one sample or in one context to other samples or contexts; also called external validity.

Question 31

What is Replication?

Answer 31

Replication refers to the duplication of scientific results, ideally in a different context or with a sample that has different characteristics.

Question 32

What is a volunteer sample?

Answer 32

A volunteer sample, or self selected sample, is a special kind of convenience sample in which participants actively choose to participate in a study.

Question 33

Explain Replication and Random Assignment?

Answer 33

Replication and random assignment to groups help overcome problems of convenience sampling. Replication involves repeating a study, ideally with different participants or in a different context, to see whether the results are consistent. With random assignment, every participant has an equal chance of being assigned to any level of the independent variable.

Question 34

Explain what is inferential Statistics?

Answer 34

Data from a sample are used to draw conclusions about the larger population.

Question 35

What is Random Sampling?

Answer 35

In random sampling, every member of the population has an equal chance of being selected for the sample.

Question 36

Which is more common, Random or Convenience Sampling?

Answer 36

In the behavioral sciences, convenience samples are far more common than random samples.

Question 37

Explain Random Assignment?

Answer 37

In random assignment, every participant has an equal chance of being assigned to one of the experimental conditions.

Question 38

Explain why replication is beneficial?

Answer 38

If a study that uses random assignment is replicated in several contexts, we can start to generalize the findings.

Question 39

Are random numbers random?

Answer 39

Random numbers may not always appear to be all that random; there may appear to be patterns.

Question 40

Explain Confirmation Bias?

Answer 40

Confirmation bias is our usually unintentional tendency to pay attention to evidence that confirms what we already believe and to ignore evidence that would disconfirm our beliefs. Confirmation biases closely follow illusory correlations.

Question 41

Explain what is illusory correlation?

Answer 41

Illusory correlation is the phenomenon of believing one sees an association between variables when no such association exists.

Question 42

Explain Personal Probability?

Answer 42

Personal probability is a person’s own judgment about the likelihood that an event will occur; also called subjective probability.

Question 43

Explain Probability?

Answer 43

Probability is the likelihood that a particular outcome—out of all possible outcomes—will occur.

Question 44

What is the expected relative frequency?

Answer 44

The expected relative frequency probability is the likelihood of an event occurring based on the actual outcome of many, many trials.

Question 45

What does a trial refer to?

Answer 45

In reference to probability, a trial refers to each occasion that a given procedure is carried out.

Question 46

Explain Confirmation Bias?

Answer 46

Human biases result from two closely related concepts. When we notice only evidence that confirms what we already believe and ignore evidence that refutes what we already believe, we’re succumbing to the confirmation bias. Confirmation biases often follow illusory correlations—when we believe we see an association between two variables, but no association exists.

Question 47

Explain Probability as a statistician.

Answer 47

In everyday life, we use the word probability very loosely—saying how likely a given outcome is, in our subjective judgment. Statisticians are referring to something very particular when they refer to probability. For statisticians, probability is the actual likelihood of a given outcome in the long run.

Question 48

What is probability theory?

Answer 48

Probability theory helps us understand that coincidences might not have an underlying meaning; coincidences are probable when we think of the vast number of occurrences in the world (billions of interactions between people daily).

Question 49

Explain an illusory correlation?

Answer 49

An illusory correlation refers to perceiving a connection where none exists. It is often followed by a confirmation bias, whereby we notice occurrences that fit with our preconceived ideas and fail to notice those that do not.

Question 50

Explain Personal Probability?

Answer 50

Personal probability refers to a person’s own judgment about the likelihood that an event will occur; also called subjective probability.

Question 51

What is Expected relative-frequency probability?

Answer 51

Expected relative-frequency probability is the likelihood of an event occurring, based on the actual outcome of many, many trials.

Question 52

What does outcomes refer to?

Answer 52

In reference to probability, outcome refers to the result of a trial.

Question 53

What does success refer to?

Answer 53

In reference to probability, success refers to the outcome for which we’re trying to determine the probability.

Question 54

Describe the probability of an event?

Answer 54

The probability of an event occurring is defined as the expected number of successes (the number of times the event occurred) out of the total number of trials (or attempts) over the long run.

Question 55

What are proportions?

Answer 55

Proportions over the short run might have many different outcomes, whereas proportions over the long run are more indicative of the underlying probabilities.

Question 56

Explain a “Control Group”?

Answer 56

A control group is a level of the independent variable that does not receive the treatment of interest in a study. It is designed to match an experimental group in all ways but the experimental manipulation itself.

Question 57

Explain “experimental group”

Answer 57

An experimental group is a level of the independent variable that receives the treatment or intervention of interest in an experiment.

Question 58

How are the control group and the experimental group treated.

Answer 58

Many experiments have an experimental group in which participants receive the treatment or intervention of interest, and a control group in which participants do not receive the treatment or intervention of interest. Aside from the intervention with the experimental group, the two groups are treated identically.

Question 59

What is the null hypothesis?

Answer 59

The null hypothesis is a statement that postulates that there is no difference between populations or that the difference is in a direction opposite of that anticipated by the researcher.

Question 60

Explain the research Hypothesis?

Answer 60

The research hypothesis is a statement that postulates that there is a difference between populations or sometimes, more specifically, that there is a difference in a certain direction, positive or negative; also called an alternative hypothesis.

Question 61

Explain an “experiment”

Answer 61

In experiments, we typically compare the average of the responses of those who receive the treatment or manipulation (the experimental group) with the average of the responses of similar people who do not receive the manipulation (the control group).

Question 62

Explain how researchers develop hypotheses?

Answer 62

Researchers develop two hypotheses: a null hypothesis, which theorizes that there is no average difference between levels of an independent variable in the population, and a research hypothesis, which theorizes that there is an average difference of some kind in the population.

Question 63

What do we mean by reject the null hypothesis?

Answer 63

Researchers can draw two conclusions: They can reject the null hypothesis and conclude that they have supported the research hypothesis or they can fail to reject the null hypothesis and conclude that they have not supported the research hypothesis.

Question 64

Explain Type I Error?

Answer 64

A Type I error involves rejecting the null hypothesis when the null hypothesis is correct.

Question 65

Describe the types of errors in hypothesis testing.

Answer 65

In hypothesis testing, there are two types of errors that we risk making. Type I errors, in which we reject the null hypothesis when the null hypothesis is true, are like false positives on a medical test; we think someone has a disease, but they really don’t. Type II errors, in which we fail to reject the null hypothesis when the null hypothesis is not true, are like false negatives on a medical test; we think someone does not have a disease, but they really do.

Question 66

Explain Type II Error?

Answer 66

A Type II error involves failing to reject the null hypothesis when the null hypothesis is false.

Question 67

What is a disadvantage of inferential statistics?

Answer 67

When we draw a conclusion from inferential statistics, there is always a chance that we are wrong.

Question 68

When do we commit a Type I error?

Answer 68

When we reject the null hypothesis, but the null hypothesis is true, we have committed a Type I error.

Question 69

When do we commit a Type II error?

Answer 69

When we fail to reject the null hypothesis, but the null hypothesis is not true, we have committed a Type II error.

Question 70

Explain Type I errors.

Answer 70

Because of the flaws inherent in research, numerous null hypotheses are rejected falsely, resulting in Type I errors.

Question 71

How does one become an educated consumer of research?

Answer 71

The educated consumer of research is aware of her or his own biases and how they might affect her or his tendency to abandon critical thinking in favor of illusory correlations and the confirmation bias.