Question 1

If you are trying to prove that A causes B, why does it make sense to have your null hypothesis be "A does not cause B"?

Accepted Answer

A)  Data cannot confirm a hypothesis. 
B)  It is easier to make strong inductive arguments when rejecting a hypothesis. 
C)  We must allow for B to cause A. 
D)  Data cannot confirm a hypothesis and it is easier to make strong inductive arguments when rejecting a hypothesis. 
A)  Data cannot confirm a hypothesis. 
B)  It is easier to make strong inductive arguments when rejecting a hypothesis. 
C)  We must allow for B to cause A. 
D)  Data cannot confirm a hypothesis and it is easier to make strong inductive arguments when rejecting a hypothesis.

Question 2

Taking samples of registered voters and asking them to disclose their preferences leading up to elections in order to assess the view of all registered voters on a particular candidate is an example of what sort of reasoning?

Accepted Answer

A)  Inductive reasoning 
B)  Deductive reasoning 
C)  Selection bias 
D)  Direct proof 
A)  Inductive reasoning 
B)  Deductive reasoning 
C)  Selection bias 
D)  Direct proof

Question 3

I give you a die, and you roll it 600 times. Let X = the number of ones observed in those 600 rolls. You build the following line of reasoning: 
Steps
1) You assume each roll is an independent, random outcome.
2) You assume the dice is fair.
3) Using your statistics book, you conclude that X is distributed binomial with mean 100 and variance 83.
4) Based on this distribution, you conclude that you would observe X  118 approximately 5% of the time.
5) You observe X = 123 and conclude that the probability that X  118 is not 5%.
6) Because the probability that X  118 is not 5%, you conclude the die is not fair.
Which of these steps is part of inductive reasoning?

Accepted Answer

A)  Step 5 
B)  Steps 4 and 6 only 
C)  All 6 steps 
D)  Steps 4, 5, and 6 only 
A)  Step 5 
B)  Steps 4 and 6 only 
C)  All 6 steps 
D)  Steps 4, 5, and 6 only

Question 4

A baseball player refuses to wash his uniform during a winning streak because he believes leaving it intact is helping his team win. What is wrong with this logic?

Accepted Answer

A)  Predictable-world bias 
B)  Availability bias 
C)  Over-optimism bias 
D)  Confirmation bias 
A)  Predictable-world bias 
B)  Availability bias 
C)  Over-optimism bias 
D)  Confirmation bias

Question 5

By showing your colleague that the conclusions of your analysis regarding the optimal pricing strategy for the firm would be the same under a variety of different assumptions regarding the price response of competitors is an example of:

Accepted Answer

A)  direct proof. 
B)  selection bias. 
C)  robustness. 
D)  degree of support. 
A)  direct proof. 
B)  selection bias. 
C)  robustness. 
D)  degree of support.

Question 6

Which of the following conclusions is an example of deductive reasoning?

Accepted Answer

A)  Given that small firms had higher growth rates in 2017, they will likely have higher growth rates in 2018. 
B)  Housing prices will rise this year, as they have in the last five years. 
C)  If industry supply is upward sloping, quantity supplied will rise as prices increase. 
D)  After only observing white-colored swans, you conclude that all swans are white. 
A)  Given that small firms had higher growth rates in 2017, they will likely have higher growth rates in 2018. 
B)  Housing prices will rise this year, as they have in the last five years. 
C)  If industry supply is upward sloping, quantity supplied will rise as prices increase. 
D)  After only observing white-colored swans, you conclude that all swans are white.

Question 7

Which of the following is an example of the use of Transposition as a method of proof for "If A, then B"?

Accepted Answer

A)  A implies B 
B)  Not B implies A 
C)  Not A implies B 
D)  Not B implies Not A 
A)  A implies B 
B)  Not B implies A 
C)  Not A implies B 
D)  Not B implies Not A

Question 8

A conclusion whose validity can be meaningfully tested using observable data is an example of:

Accepted Answer

A)  inductive reasoning. 
B)  data mining. 
C)  empirically testable conclusion. 
D)  direct proof. 
A)  inductive reasoning. 
B)  data mining. 
C)  empirically testable conclusion. 
D)  direct proof.

Question 9

The primary difference between the population and a particular data sample is that:

Accepted Answer

A)  the population is the universe of potential observations, while a sample is a subset. 
B)  the data sample is random. 
C)  the data sample is representative. 
D)  the data sample is large enough to draw inferences. 
A)  the population is the universe of potential observations, while a sample is a subset. 
B)  the data sample is random. 
C)  the data sample is representative. 
D)  the data sample is large enough to draw inferences.

Question 10

Why is it that inductive reasoning does not involve a proof?

Accepted Answer

A)  We cannot prove the general from the specific. 
B)  Mathematical induction only applies to deductive reasoning. 
C)  We cannot prove the specific from the general. 
D)  None of the answers is correct. 
A)  We cannot prove the general from the specific. 
B)  Mathematical induction only applies to deductive reasoning. 
C)  We cannot prove the specific from the general. 
D)  None of the answers is correct.

If you are trying to prove that A causes B, why does it make sense to have your null hypothesis be "A does not cause B"?

Taking samples of registered voters and asking them to disclose their preferences leading up to elections in order to assess the view of all registered voters on a particular candidate is an example of what sort of reasoning?

A baseball player refuses to wash his uniform during a winning streak because he believes leaving it intact is helping his team win. What is wrong with this logic?

By showing your colleague that the conclusions of your analysis regarding the optimal pricing strategy for the firm would be the same under a variety of different assumptions regarding the price response of competitors is an example of:

Which of the following conclusions is an example of deductive reasoning?

Which of the following is an example of the use of Transposition as a method of proof for "If A, then B"?

A conclusion whose validity can be meaningfully tested using observable data is an example of:

The primary difference between the population and a particular data sample is that:

Why is it that inductive reasoning does not involve a proof?

The Roles of Data and Predictive Analytics in Business

Reasoning From Sample to Population

The Scientific Method: The Gold Standard for Establishing Causality

Linear Regression As a Fundamental Descriptive Tool

Correlation Vs Causality in Regression Analysis

Basic Methods for Establishing Causal Inference

Advanced Methods for Establishing Causal Inference

Prediction for a Dichotomous Variable

Identification and Data Assessment

Filters

Exam 2: Reasoning With Data

If you are trying to prove that A causes B, why does it make sense to have your null hypothesis be "A does not cause B"?

Taking samples of registered voters and asking them to disclose their preferences leading up to elections in order to assess the view of all registered voters on a particular candidate is an example of what sort of reasoning?

A baseball player refuses to wash his uniform during a winning streak because he believes leaving it intact is helping his team win. What is wrong with this logic?

By showing your colleague that the conclusions of your analysis regarding the optimal pricing strategy for the firm would be the same under a variety of different assumptions regarding the price response of competitors is an example of:

Which of the following conclusions is an example of deductive reasoning?

Which of the following is an example of the use of Transposition as a method of proof for "If A, then B"?

A conclusion whose validity can be meaningfully tested using observable data is an example of:

The primary difference between the population and a particular data sample is that:

Why is it that inductive reasoning does not involve a proof?

The Roles of Data and Predictive Analytics in Business

Reasoning From Sample to Population

The Scientific Method: The Gold Standard for Establishing Causality

Linear Regression As a Fundamental Descriptive Tool

Correlation Vs Causality in Regression Analysis

Basic Methods for Establishing Causal Inference

Advanced Methods for Establishing Causal Inference

Prediction for a Dichotomous Variable

Identification and Data Assessment

Filters