Question 1

Hypothesis testing is a function of inferential statistics.

Accepted Answer

Hypothesis testing is a critical component of inferential statistics, which involves drawing conclusions about a population based on sample data. Hypothesis testing enables researchers to determine the probability that the observed differences between groups are due to chance or whether they are statistically significant.

Question 2

Data analysis has important implications for conclusion validity.

Accepted Answer

Data analysis plays a crucial role in ensuring the validity of conclusions drawn from a study. Proper data analysis helps to identify any errors or biases in the data, clarify relationships between variables, and support or refute hypotheses. However, poor data analysis can lead to inaccurate or misleading findings, undermining the conclusions drawn from a study.

Question 3

A low strength signal is associated with ____.
A)low reliability of the measure
B)low strength of the intervention
C)poor reliability of treatment implementation
D)random heterogeneity of respondents

Accepted Answer

B

Question 4

Both qualitative and quantitative analyses have underlying assumptions.

Accepted Answer

Both qualitative and quantitative analyses are based on certain assumptions, such as the assumption that the data is accurate and representative or the assumption that the research methods used are valid and reliable. These assumptions influence the way data is collected, analyzed and interpreted.

Question 5

If a researcher sets alpha at .05,the probability of a Type II error ____.
A)= .05
B)= .10
C)=  .95​
D)cannot be calculated from alpha

Accepted Answer

The probability of a Type II error, denoted as beta (β), is not directly determined by the alpha level (α), which is the probability of a Type I error. Alpha and beta are related to different aspects of hypothesis testing, and the value of beta depends on factors such as the effect size, sample size, and the statistical power of the test, not directly on the alpha level.

Question 6

Data transformation involves modifying data so they are more consistent with the theory.

Accepted Answer

Data transformation involves modifying data to improve its quality, consistency, and usability, not necessarily to align with a specific theory.

Question 7

If a researcher sets alpha at .05,the probability of a Type I error ____.
A)= .05
B)= .10
C)=  .95
D)cannot be calculated from alpha

Accepted Answer

Setting alpha at .05 means that there is a 5% probability of rejecting the null hypothesis when it is actually true, which is the definition of a Type I error. Therefore, the probability of a Type I error is equal to alpha, which is .05 in this case.

Question 8

In terms of signal and noise,a Type II error represents a ____.
A)false positive
B)false negative
C)true positive
D)true negative

Accepted Answer

A Type II error occurs when we fail to reject a null hypothesis that is actually false. This means we conclude that there is no effect, when in reality there is one. Therefore, a Type II error represents a false negative, where we fail to detect a true signal (the effect) among the noise (the variability in our data).

Question 9

Data preparation involves making graphs that show the distributions of the data.

Accepted Answer

Data preparation involves cleaning, organizing, and transforming data, but not necessarily making graphs. Graphs may be part of data visualization, which comes after data preparation.

Question 10

Many statistical analyses are based on the assumption that the data are distributed normally.

Accepted Answer

Many statistical analyses, such as t-tests and ANOVA, assume that the data being analyzed are normally distributed. This assumption is typically checked using statistical tests such as the Shapiro-Wilk test or visual inspection of a histogram or Q-Q plot. However, there are also techniques that can be used to analyze non-normal data, such as non-parametric tests or transformations of the data.

Question 11

Descriptive statistics deal with whether or not a statistically significant difference exists.

Accepted Answer

Descriptive statistics are used to describe and summarize data, such as measures of central tendency and variability. They do not assess whether or not a statistically significant difference exists, that is the realm of inferential statistics.

Question 12

Data graphs are a type of ____ statistics.
A)causal
B)descriptive
C)inferential
D)deductive

Accepted Answer

Data graphs are a form of descriptive statistics as they present and summarize data in a visual format to show patterns, trends, and relationships among variables. Descriptive statistics are used to summarize and describe the essential features of the data, such as measures of central tendency, variability, and shape. Causal statistics involves identifying cause-and-effect relationships between variables, inferential statistics involves making predictions and generalizations based on a sample of data to a larger population, and deductive statistics involves testing hypotheses using logical reasoning and deductive reasoning.

Question 13

Random irrelevancies in a setting are most related to a ____.
A)weaker signal
B)lower level of noise
C)stronger signal
D)higher level of noise

Accepted Answer

Random irrelevancies in a setting are distractions or disturbances, also known as noise. Therefore, higher levels of noise (choice D) are most related to random irrelevancies in a setting.

Question 14

The branch of statistics that allows researchers to test their hypotheses is called ____ statistics.
A)causal
B)descriptive
C)inferential
D)deductive

Accepted Answer

Inferential statistics allows researchers to draw conclusions about populations based on the data collected from a sample, and to determine the probability that the results are due to chance or other factors. This branch of statistics helps researchers test their hypotheses and make inferences about the larger population based on the data collected.

Question 15

A Type I error occurs when an effect exists,but it is not found in the research.

Accepted Answer

A Type I error occurs when no effect exists, but the research erroneously concludes that there is one.

Question 16

Beryl is conducting an observational study and has trained two raters to use the rating instrument.Both of the raters drop out just after the study has begun,so Beryl has to substitute in less well-trained raters.Beryl has increased the risk of ____.
A)low reliability of the measure
B)low strength of the intervention
C)poor reliability of treatment implementation
D)random irrelevancies in the setting

Accepted Answer

The fact that Beryl had to substitute in less well-trained raters increases the risk of low reliability of the measure. This is because inter-rater reliability is a key component of the reliability of measures used in observational studies. If the raters are not well-trained, their ratings may not be consistent, leading to low inter-rater reliability and ultimately low reliability of the measure.

Question 17

Conclusion validity applies to quantitative but not qualitative research.

Accepted Answer

Conclusion validity is relevant to both quantitative and qualitative research. It refers to the ability of a research study to draw accurate and valid conclusions based on the data collected. Both types of research can be subject to potential threats to conclusion validity, such as sampling bias or measurement error.

Question 18

In terms of signal and noise,a Type I error represents a ____.
A)false positive
B)false negative
C)true positive
D)true negative

Accepted Answer

A Type I error occurs when the null hypothesis is rejected even though it is true, which means that the result is a false positive. This is an error in which we falsely conclude that there is a relationship or difference between variables when in fact there is none.

Question 19

The probability of a type I error is equal to ____.
A)alpha
B)1 - alpha
C)2 * alpha
D)alpha / statistical power

Accepted Answer

The probability of a type I error is denoted by the Greek letter alpha (α) and is given by the significance level. Therefore, the correct answer is A) alpha.

Question 20

Conclusion validity is relevant whenever you are looking at relationships between variables.

Accepted Answer

Conclusion validity concerns the extent to which we can validly draw conclusions about the relationship between variables based on the data collected.

Quiz 11: Introduction to Data Analysis

Hypothesis testing is a function of inferential statistics.

Data analysis has important implications for conclusion validity.

A low strength signal is associated with ____.

Both qualitative and quantitative analyses have underlying assumptions.

If a researcher sets alpha at .05,the probability of a Type II error ____.

Data transformation involves modifying data so they are more consistent with the theory.

If a researcher sets alpha at .05,the probability of a Type I error ____.

In terms of signal and noise,a Type II error represents a ____.

Data preparation involves making graphs that show the distributions of the data.

Many statistical analyses are based on the assumption that the data are distributed normally.

Descriptive statistics deal with whether or not a statistically significant difference exists.

Data graphs are a type of ____ statistics.

Random irrelevancies in a setting are most related to a ____.

The branch of statistics that allows researchers to test their hypotheses is called ____ statistics.

A Type I error occurs when an effect exists,but it is not found in the research.

Beryl is conducting an observational study and has trained two raters to use the rating instrument.Both of the raters drop out just after the study has begun,so Beryl has to substitute in less well-trained raters.Beryl has increased the risk of ____.

Conclusion validity applies to quantitative but not qualitative research.

In terms of signal and noise,a Type I error represents a ____.

The probability of a type I error is equal to ____.

Conclusion validity is relevant whenever you are looking at relationships between variables.