$35.00

xercise | FieldStat4 1.MC.001. | |

Classify each of the following variables as either nominal or continuous. - age
- gender
- height
- race
| ||

Author’s Notes | ||

Multiple Choice Part (a) Options (correct choice comes first) | Feedback (rejoinder) for this choice | |

continuous | Correct | |

nominal | Incorrect | |

Multiple Choice Part (b) Options (correct choice comes first) | Feedback (rejoinder) for this choice | |

nominal | Correct | |

continuous | Incorrect | |

Multiple Choice Part (c) Options (correct choice comes first) | Feedback (rejoinder) for this choice | |

continuous | Correct | |

nominal | Incorrect | |

Multiple Choice Part (d) Options (correct choice comes first) | Feedback (rejoinder) for this choice | |

nominal | Correct | |

continuous | Incorrect | |

Exercise | FieldStat4 1.MC.002. | ||

A café owner decided to calculate how much revenue he gained from lattes each month. What type of variable would the amount of revenue gained from lattes be? | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

continuous | Yes, the amount of revenue gained from lattes would be a continuous variable. A continuous variable is one for which, within the limits the variable ranges, any value is possible. Indeed, it is meaningful to speak of £107,543 (or dollars, euros etc.) (see Section 1.5.1.2). | ||

categorical | This is incorrect because categorical variables are variables in which entities are divided into distinct categories (see Section 1.5.1.2). | ||

discrete | This is incorrect because a discrete variable can only take on certain values (usually whole numbers) (see Section 1.5.1.2). | ||

nominal | This is incorrect because a nominal variable is one that describes a name or category (see Section 1.5.1.2). | ||

Exercise | FieldStat4 1.MC.003. | ||

A café owner wanted to compare how much revenue he gained from lattes across different months of the year. What type of variable is ‘month’? | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

categorical | Yes, this is correct because months of the year are divided into distinct categories (seeSection 1.5.1.2). | ||

dependent | This is incorrect because a ‘dependent variable’ represents the output or effect (seeSection 1.5.1.1). Revenue would be the dependent variable. | ||

interval | This is incorrect because interval variables can be measured along a continuum and they have a numerical value (for example, temperature measured in degrees Celsius or Fahrenheit) (seeSection 1.5.1.2). | ||

continuous | This is incorrect, a continuous variable is one for which within the limits the variable ranges, any value is possible (seeSection 1.5.1.2). | ||

Exercise | FieldStat4 1.MC.004. | ||

Which of the following best describes a confounding variable? | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

A variable that affects the outcome being measured as well as, or instead of, the independent variable. | Yes, this is correct because a confounding variable is an unforeseen and unaccounted-for variable that jeopardizes reliability and validity of an experiment's outcome (see Section 1.5.5). | ||

A variable that is manipulated by the experimenter. | This is incorrect because a confounding variable is an unforeseen and unaccounted-for variable that jeopardizes reliability and validity of an experiment's outcome (see Section 1.5.5). | ||

A variable that has been measured using an unreliable scale. | This is incorrect because although a confounding variable could be measured using an unreliable scale, this is not its defining feature – it could equally be measured using a reliable scale, or not measured at all (see Section 1.5.5.2). | ||

A variable that is made up only of categories. | This is incorrect, because although a confounding variable could be categorical, this is not its defining feature – it could equally be a continuous variable. A variable that is made up only of categories is known as a categorical variable (see Section 1.5.1.2). | ||

Exercise | FieldStat4 1.MC.005. | ||

A demand characteristic is: | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

When a person responds in an experiment in a way that is consistent with their beliefs about how the experimenter would like them to behave. | Yes, this is correct; a demand characteristic refers to an experimental artefact where participants form an interpretation of the experiment's purpose and unconsciously change their behaviour to fit that interpretation. | ||

When the experimenter’s behaviour affects the results of an experiment. | This describes an experimenter effect and is, therefore, incorrect. | ||

A personality trait that affects the results of an experiment in an undesirable way. | This is incorrect; a demand characteristic refers to an experimental artefact where participants form an interpretation of the experiment's purpose and unconsciously change their behaviour to fit that interpretation. | ||

A personality trait that makes a participant likely to find an experiment too demanding. | This is incorrect; a demand characteristic refers to an experimental artefact where participants form an interpretation of the experiment's purpose and unconsciously change their behaviour to fit that interpretation. | ||

Exercise | FieldStat4 1.MC.006. | ||

If a test is valid, what does this mean? | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

The test measures what it claims to measure. | Yes, this is correct. For more information on validity and reliability see Section 1.5.3. | ||

The test will give consistent results. | This is incorrect. This statement describes reliability. For more information on reliability and validity see Section 1.5.3. | ||

The test has internal consistency. | This is incorrect. Internal consistency is typically a measure based on the correlations between different items on the same test (or the same subscale on a larger test). It measures whether several items that propose to measure the same general construct produce similar scores. For example, if a respondent expressed agreement with the statements ‘I like rock music’ and ‘I've enjoyed listening to rock music in the past’, and disagreement with the statement ‘I hate rock music’, this would be indicative of good internal consistency of the test (see Section 1.5.3). | ||

The test measures a useful construct or variable. | This is incorrect. A test can measure something useful but still not be valid (see Section 1.5.3). | ||

Exercise | FieldStat4 1.MC.007. | ||

When questionnaire scores predict or correspond with external measures of the same construct that the questionnaire measures it is said to have: | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

Criterion validity | Yes, this is correct. Criterion validity is a measure of how well a particular measure/questionnaire compares with other measures or outcomes (the criteria) that are already established as being valid. For example, IQ tests are often validated against measures of academic performance (the criterion) (see Section 1.5.3). | ||

Factorial validity | This is incorrect. Factorial validity refers to the clustering of correlations of responses by groupings of items in the questionnaire. Factor analysis can be used for this purpose. Basically, the groupings must make intuitive sense to the investigator otherwise the questionnaire has poor factorial validity (see Section 1.5.3). | ||

Ecological validity | This is incorrect. For a research study to possess ecological validity, the methods, materials and setting of the study must approximate the real-life situation that is under investigation (see Section 1.5.3). | ||

Content validity | This is incorrect. Content validity refers to the degree to which individual items on a questionnaire/measure represent the construct being measured, and cover the full range of the construct (see Section 1.5.3). | ||

Exercise | FieldStat4 1.MC.008. | ||

When the results of an experiment can be applied to real-world conditions, that experiment is said to have: | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

Ecological validity | Yes, this is correct. For a research study to possess ecological validity, the methods, materials and setting of the study must approximate the real-life situation that is under investigation (see Section 1.5.3). | ||

Factorial validity | This is incorrect. Factorial validity refers to the clustering of correlations of responses by groupings of items in the questionnaire. Factor analysis can be used for this purpose. Basically, the groupings must make intuitive sense to the investigator otherwise the questionnaire has poor factorial validity (see Section 1.5.3). | ||

Content validity | This is incorrect. Content validity refers to the degree to which individual items on a questionnaire/measure represent the construct being measured, and cover the full range of the construct (see Section 1.5.3). | ||

Criterion validity | This is incorrect. Criterion validity is a measure of how well a particular measure/questionnaire compares with other measures or outcomes (the criteria) that are already established as being valid. For example, IQ tests are often validated against measures of academic performance (the criterion) (see Section 1.5.3). | ||

Exercise | FieldStat4 1.MC.009. | ||

A variable manipulated by a researcher is known as: | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

An independent variable | Yes, this is correct. An independent variable (or predictor variable) is a variable that is thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated (see Section 1.5.1.1). | ||

A dependent variable | This is incorrect. A dependent variable is a variable that is thought to be affected by changes in an independent variable. You can think of this variable as an outcome (see Section 1.5.1.1) | ||

A confounding variable | This is incorrect. A confounding variable is a variable which has an unintentional effect on the dependent variable. When carrying out experiments we attempt to control these extraneous variables; however, there is always the possibility that one of these variables is not controlled and if this affects the dependent variable in a systematic way, we call this a confounding variable (see Section 1.5.1.1). | ||

A discrete variable | This is incorrect. A discrete variable can take on only certain values (usually whole numbers) on the scale (see Jane Superbrain Box 1.3 and Section 1.5.1.1). | ||

Exercise | FieldStat4 1.MC.010. | ||

A variable that measures the effect that manipulating another variable has is known as: | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

A dependent variable | Yes, this is correct. A dependent variable (or outcome variable) is a variable that is thought to be affected by changes in an independent variable (see Section 1.5.1.1). | ||

A confounding variable | This is incorrect. A confounding variable is a variable which has an unintentional effect on the dependent variable. When carrying out experiments we attempt to control these extraneous variables; however, there is always the possibility that one of these variables is not controlled and if this affects the dependent variable in a systematic way, we call this a confounding variable (see Section 1.5.1.1). | ||

A predictor variable | This is incorrect. An predictor variable is a variable that is thought to predict another variable (see Section 1.5.1.1). | ||

An independent variable | This is incorrect. An independent variable is a variable that is thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated (see Section 1.5.1.1). | ||

Exercise | FieldStat4 1.MC.011. | ||

A predictor variable is another name for: | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

An independent variable | Yes, this is correct. An independent variable (or predictor variable) is a variable that is thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated (see Section 1.5.1.1). | ||

A dependent variable | This is incorrect. A dependent variable is a variable that is thought to be affected by changes in an independent variable. You can think of this variable as an outcome (see Section 1.5.1.1). | ||

A confounding variable | This is incorrect. A confounding variable is a variable which has an unintentional effect on the dependent variable. When carrying out experiments we attempt to control these extraneous variables; however; there is always the possibility that one of these variables is not controlled and if this affects the dependent variable in a systematic way, we call this a confounding variable (see Section 1.5.1.1). | ||

A discrete variable | This is incorrect. A discrete variable can take on only certain values (usually whole numbers) on the scale (see Jane Superbrain Box 1.3 and Section 1.5.1.1). | ||

Exercise | FieldStat4 1.MC.012. | ||

The discrepancy between the numbers used to represent something that we are trying to measure and the actual value of what we are measuring is called: | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

Measurement error | Yes, this is correct. It’s one thing to measure variables, but it’s another thing to measure them accurately. (see Section 1.5.2). | ||

Reliability | This is incorrect. Reliability refers to whether an instrument can be interpreted consistently across different situations (see Sections 1.5.2 and 1.5.3). | ||

The ‘fit’ of the model | This is incorrect. The ‘fit’ of the model is the degree to which a statistical model represents the data collected (see Section 1.5.2). | ||

Variance | This is incorrect. The variance is the average error between the mean and the observations made (see Section 1.5.2). | ||

Exercise | FieldStat4 1.MC.013. | ||

What kind of variable is IQ, measured by a standard IQ test? | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

Continuous | Yes, this is correct. A continuous variable is one for which, within the limits the variable ranges, any value is possible (see Section 1.5.1.2). | ||

Categorical | This is incorrect. Categorical variables are variables in which entities are divided into distinct categories (see Section 1.5.1.2). | ||

Discrete | This is incorrect because a discrete variable can only take on certain values (usually whole numbers) (see Section 1.5.1.2). | ||

Nominal | This is incorrect because a nominal variable is one that describes a name or category (see Section 1.5.1.2). | ||

Exercise | FieldStat4 1.MC.014. | ||

A frequency distribution in which low scores are most frequent (i.e. bars on the graph are highest on the left hand side) is said to be: | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

Positively skewed | Yes, this is correct. In a positively skewed distribution the frequent scores are clustered at the lower end and the tail points towards the higher or more positive scores (see Section 1.6.1). | ||

Leptokurtic | This is incorrect. A leptokurtic distribution describes a distribution with positive kurtosis, it has many scores in the tails (a so-called heavy-tailed distribution) and is pointy (see Section 1.6.1). | ||

Platykurtic | This is incorrect. A platykurtic distribution describes a distribution with negative kurtosis and it is relatively thin in the tails (has light tails) and tends to be flatter than normal (see Section 1.6.1). | ||

Negatively skewed | This is incorrect. In a negatively skewed distribution the frequent scores are clustered at the higher end and the tail points towards the lower or more negative scores (see Section 1.6.1). | ||

Exercise | FieldStat4 1.MC.015. | ||

A frequency distribution in which high scores are most frequent (i.e. bars on the graph are highest on the right hand side) is said to be: | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

Negatively skewed | Yes, this is correct. In a negatively skewed distribution the frequent scores are clustered at the higher end and the tail points towards the lower or more negative scores (see Section 1.6.1). | ||

Positively skewed | This is incorrect. In a positively skewed distribution the frequent scores are clustered at the lower end and the tail points towards the higher or more positive scores (see Section 1.6.1). | ||

Leptokurtic | This is incorrect. A leptokurtic distribution describes a distribution with positive kurtosis, it has many scores in the tails (a so-called heavy-tailed distribution) and is pointy (see Section 1.6.1). | ||

Platykurtic | This is incorrect. A platykurtic distribution describes a distribution with negative kurtosis and it is relatively thin in the tails (has light tails) and tends to be flatter than normal (see Section 1.6.1). | ||

Exercise | FieldStat4 1.MC.016. | ||

A frequency distribution in which there are too many scores at the extremes of the distribution said to be: | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

Platykurtic | Yes, this is correct. A platykurtic distribution describes a distribution with negative kurtosis and it is relatively thin in the tails (has light tails) and tends to be flatter than normal (see Section 1.6.1). | ||

Positively skewed | This is incorrect. In a positively skewed distribution the frequent scores are clustered at the lower end and the tail points towards the higher or more positive scores (see Section 1.6.1). | ||

Leptokurtic | This is incorrect. A leptokurtic distribution describes a distribution with positive kurtosis, it has many scores in the tails (a so-called heavy-tailed distribution) and is pointy (see Section 1.6.1). | ||

Negatively skewed | This is incorrect. In a negatively skewed distribution the frequent scores are clustered at the higher end and the tail points towards the lower or more negative scores (see Section 1.6.1). | ||

Exercise | FieldStat4 1.MC.017. | ||

A frequency distribution in which there are too few scores at the extremes of the distribution said to be: | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

Leptokurtic | Yes, this is correct. A leptokurtic distribution describes a distribution with positive kurtosis, it has many scores in the tails (a so-called heavy-tailed distribution) and is pointy (see Section 1.6.1). | ||

Positively skewed | This is incorrect. In a positively skewed distribution the frequent scores are clustered at the lower end and the tail points towards the higher or more positive scores (see Section 1.6.1). | ||

Platykurtic | This is incorrect. A platykurtic distribution describes a distribution with negative kurtosis and it is relatively thin in the tails (has light tails) and tends to be flatter than normal (see Section 1.6.1). | ||

Negatively skewed | This is incorrect. In a negatively skewed distribution the frequent scores are clustered at the higher end and the tail points towards the lower or more negative scores (see Section 1.6.1). | ||

Exercise | FieldStat4 1.MC.018. | ||

Which of the following is designed to compensate for practice effects? | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

Counterbalancing | Yes, this is correct. Although practice effects are impossible to eliminate completely, we can ensure that they produce no systematic variation between our conditions by counterbalancing the order in which a person participates in a condition (see Section 1.5.6). | ||

A repeated measured design | This is incorrect. Practice effects are an issue in repeated measures designs (see Sections 1.5.2 and 1.5.6). | ||

Giving participants a break between tasks | This is incorrect. This technique is used to compensate for boredom effects (see Section 1.5.6). | ||

A control condition | This is incorrect. A control condition provides you with a reference point to determine what change (if any) occurred when a variable was modified (see Section 1.5.6). | ||

Exercise | FieldStat4 1.MC.019. | ||

Variation due to variables that have not been measured is known as: | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

Unsystematic variation | Yes, this is correct. Unsystematic variation results from random factors that exist between the experimental conditions (such as natural differences in ability, the time of day, etc.) (see Section 1.5.5.2). | ||

Homogenous variance | This is incorrect. The assumption of homogeneity of variance is that the variance within each of the populations is equal (see Section 1.5.5.2). | ||

Systematic variation | This is incorrect. Systematic variation is due to the experimenter doing something in one condition but not in the other condition (see Section 1.5.5.2). | ||

Residual variance | This is incorrect. Residual variance helps us confirm how well a regression line that we constructed fits the actual data set. The smaller the variance, the more accurate the predictions are (see Section 1.5.5.2). | ||

Exercise | FieldStat4 1.MC.020. | ||

The purpose of a control condition is to: | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

Allow inferences about cause. | Yes, this is correct. A properly constructed control condition provides you with a reference point to determine what change (if any) occurred when a variable was modified (see Section 1.5.6). | ||

Control for participant characteristics. | This is incorrect. Randomization helps control for participant characteristics (see Sections 1.5.2 and 1.5.3) | ||

Show up relationships between predictor variables. | This is incorrect (see Section 1.5.2). | ||

Rule out a | This is incorrect. | ||

Exercise | FieldStat4 1.MC.021. | ||

If the scores on a test have a mean of 26 and a standard deviation of 4, what is the | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

–2 | Yes, this is correct; z scores are calculated by subtracting the mean from the score (18 – 26) = –8 and dividing your answer by the standard deviation (–8 / 4) = –2 (see Section 1.6.4). | ||

11 | This is incorrect; z scores are calculated by subtracting the mean from the score (18 – 26) = –8 and dividing your answer by the standard deviation (–8 / 4) = –2 (see Section 1.6.4). | ||

2 | This is incorrect; z scores are calculated by subtracting the mean from the score (18 – 26) = –8 and dividing your answer by the standard deviation (–8 / 4) = –2 (see Section 1.6.4). | ||

–1.41 | This is incorrect; z scores are calculated by subtracting the mean from the score (18 – 26) = –8 and dividing your answer by the standard deviation (–8 / 4) = –2 (see Section 1.6.4). | ||

Exercise | FieldStat4 1.MC.022. | ||

What is a scientific journal? | |||

Author’s Notes | |||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | ||

A collection of articles written by scientists that have been peer reviewed. | Yes, this is correct. Scientific journals contain articles that have been peer reviewed, in an attempt to ensure that articles meet the journal's standards of quality, and scientific validity (see Section 1.7.1). | ||

A notebook kept by scientists containing important details of all their own experimental research for future reference. | This is incorrect. Scientific journals contain articles that have been peer reviewed, in an attempt to ensure that articles meet the journal's standards of quality, and scientific validity (see Section 1.7.1). | ||

A collection of articles written by scientists that have not yet been reviewed by other scientists in the field. | This is incorrect. Scientific journals contain articles that have been peer reviewed, in an attempt to ensure that articles meet the journal's standards of quality, and scientific validity (see Section 1.7.1). | ||

A piece of scientific research that has not yet been published. | This is incorrect. Scientific journals contain articles that have been peer reviewed, in an attempt to ensure that articles meet the journal's standards of quality, and scientific validity (see Section 1.7.1). | ||

Exercise | FieldStat4 1.MC.023. | |

- The standard deviation is the square root of the:
| ||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | |

variance | Yes, this is the correct. The sum of squares, variance and standard deviation are all measures of the dispersion or spread of data around the mean (see Section 1.6.3). | |

coefficient of determination | This is incorrect. The coefficient of determination is the correlation coefficient squared; it is a measure of the amount of variability in one variable that is shared by the other (see Section 1.6.3 and 7.4.2.2). | |

sum of squares | This is incorrect. The sum of squared errors is the sum of the squared deviances (see Section 1.6.3). | |

range | This is incorrect. The range is the spread, or dispersion of scores in the data. To calculate the range you take the largest score and subtract from it the smallest score (see Section 1.6.3). | |

Exercise | FieldStat4 1.MC.024. | |

Below is a histogram of ratings of Britney Spears’s CD, | ||

Author’s Notes | ||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | |

The modal score is 5. | Yes, this is the correct. The tallest bar represents the mode, and this bar is for a rating of 5 (see Section 1.6.2). | |

The data are normally distributed. | This is incorrect. If anything, this distribution looks somewhat bimodal (see Section 1.6.2). | |

The median rating was 2. | This is incorrect. There are more scores to the right of 2 than to the left, suggesting that the median is greater than 2 (see Section 1.6.2). | |

The data are leptokurtic. | This is incorrect. A leptokurtic distribution has too few scores in the tails; this distribution appears to have too many (see Section 1.6.2). | |

Exercise | FieldStat4 1.MC.025. | |

What is the standard deviation? | ||

Author’s Notes | ||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | |

A measure of the dispersion or spread of data around the mean. | Yes, this is correct. Small standard deviations (relative to the value of the mean itself) indicate that data points are close to the mean. A large standard deviation (relative to the mean) indicates that the data points are distant from the mean (see Section 1.6.3). | |

A measure of the relationship between two variables. | This is incorrect (see Section 1.6.3). | |

The variance squared. | This is incorrect. The standard deviation is the square root of the variance (see Section 1.6.3). | |

The degree to which scores cluster at the ends of the distribution. | This is incorrect. This is a description of kurtosis (see Section 1.6.3). | |

Exercise | FieldStat4 1.MC.026. | |

Complete the following sentence: A small standard deviation (relative to the value of the mean itself) ( | ||

Author’s Notes | ||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | |

indicates that data points are close to the mean (i.e. the mean is a good fit of the data). | Yes, this is the correct (see Section 1.6.3). | |

indicates that the data points are distant from the mean. | This is incorrect (see Section 1.6.3). | |

indicates that the mean is a poor fit of the data. | This is incorrect. It actually indicates the opposite! (see Section 1.6.3). | |

indicates that you should analyse your data with a non-parametric test. | This is incorrect. You should use non-parametric tests when your data deviate from a normal distribution (see Section 1.6.3). | |

Exercise | FieldStat4 1.MC.027. | |

Complete the following sentence: A large standard deviation (relative to the value of the mean itself) ( | ||

Author’s Notes | ||

Multiple Choice Options (correct choice comes first) | Feedback (rejoinder) for this choice | |

indicates that the data points are distant from the mean (i.e. the mean is a poor fit of the data). | Yes, this is the correct (see Section 1.6.3). | |

indicates that the data points are close to the mean. | This is incorrect (see Section 1.6.3). | |

indicates that the mean is a good fit of the data. | This is incorrect. It actually indicates the opposite! (see Section 1.6.3). | |

indicates that you should analyse your data with a parametric test. | This is incorrect (see Section 1.6.3). |

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