DSGym/DSGym-Tasks huggingface dataset:
Context: In this experiment, each individual was asked to be a seller of an iPod (a product commonly used to store music on before smart phones...). The participant received $10 + 5% of the sale price for participating. The iPod they were selling had frozen twice in the past inexplicably but otherwise worked fine. The prospective buyer starts off and then asks one of three final questions, depending on the seller's treatment group. The experiment data is in the CSV file ask.csv.

The three possible questions:
General: What can you tell me about it?
Positive Assumption: It doesn't have any problems, does it?
Negative Assumption: What problems does it have?
The outcome variable is whether or not the participant discloses or hides the problem with the iPod.

The hypothesis test for the iPod experiment is really about assessing whether there is statistically significant evidence that the success each question had on getting the participant to disclose the problem with the iPod. In other words, the goal is to check whether the buyer's question was independent of whether the seller disclosed a problem.

Question: If the questions were actually equally effective, meaning about 27.85% of respondents would disclose the freezing issue regardless of what question they were asked, about how many sellers would we expect to hide the freezing problem from the Positive Assumption group? Please round to the nearest hundredth.
Answer: 52.67
Topics: Statistics, Categorial data, Chi-square

Question: Given the null hypothesis that the questions had no impact on the sellers in the experiment, calculate the chi-square (X2) statistic. Please round to the nearest hundredth.
Answer: 40.13
Topics: Statistics, Categorial data, Chi-square

Question: Given the null hypothesis that the questions had no impact on the sellers in the experiment, how many degrees of freedom should be associated with the chi-square distribution used for X2?
Answer: 2
Topics: Statistics, Categorial data, Chi-square
Context: Three treatments were compared to test their relative efficacy (effectiveness) in treating Type 2 Diabetes in patients aged 10-17 who were being treated with metformin. The primary outcome was lack of glycemic control (or not); lacking glycemic control means the patient still needed insulin, which is not the preferred outcome for a patient.
Each of the 699 patients in the experiment was randomized to one of the following treatments: (1) continued treatment with metformin (coded as met), (2) formin combined with rosiglitazone (coded as rosi), or (3) a lifestyle-intervention program (coded as lifestyle). Each patient had a primary outcome, which was either lacked glycemic control (failure) or did not lack that control (success).

Question: Given the null hypothesis that there is no difference in the effectiveness of the three treatments, compute the chi-square test statistic. Please round to the nearest hundredth.
Answer: 8.16
Topics: Statistics, Categorial data, Chi-square

Question: Given the null hypothesis that there is no difference in the effectiveness of the three treatments, conduct a chi-square test. Will you accept or reject the hypothesis at the 5% significance level? Please answer with "accept" or "reject".
Answer: reject
Topics: Statistics, Categorial data, Chi-square

Question: Given the null hypothesis that there is no difference in the effectiveness of the three treatments, conduct a chi-square test. Identify the p-value. Please round to 3 decimal places.
Answer: 0.017
Topics: Statistics, Categorial data, Chi-square
Context: A news article reports that “Americans have differing views on two potentially inconvenient and invasive practices that airports could implement to uncover potential terrorist attacks.” This news piece was based on a survey conducted among a random sample of 1,137 adults nationwide, interviewed by telephone November 7-10, 2010, where one of the questions on the survey was “Some airports are now using ‘full-body’ digital x-ray machines to electronically screen passengers in airport security lines. Do you think these new x-ray machines should or should not be used at airports?” The responses are in full_body_scan.csv.

Question: Conduct an appropriate hypothesis test evaluating whether there is a difference in the proportion of Republicans and Democrats who think the full-body scans should be applied in airports with a significance level of 5%. Please answer with "there is difference" or "no difference".
Answer: no difference
Topics: Statistics, Categorial data

Question: The differences in each political group may be due to chance. Under the null hypothesis of independence between an individual's party affiliation and his support of full-body scans, how many Republicans would you expect to not support the use of full-body scans? Please round to the nearest hundredth.
Answer: 47.55
Topics: Statistics, Categorial data
Context: A survey asked 827 randomly sampled registered voters in California "Do you support? Or do you oppose? Drilling for oil and natural gas off the Coast of California? Or do you not know enough to say?" The survey data is in offshore_drilling.csv.

Question: Given the hypothesis that the proportion of college graduates who do not have an opinion on this issue is equal to that of non-college graduates, will you accept or reject the hypothesis with a significance level of 5%? Please answer with "accept" or "reject".
Answer: reject
Topics: Statistics, Categorial data

Question: Given the hypothesis that the proportion of college graduates who support off-shore drilling in California is equal to that of non-college graduates, calculate the p-value for the hypothesis. Please round to 4 decimal places.
Answer: 0.6966
Topics: Statistics, Categorial data

Question: Given the null hypothesis that the opinion of college grads and non-grads is not different on the topic of drilling for oil and natural gas off the coast of California, compute the chi-square test statistic. Please round to the nearest hundredth.
Answer: 11.47
Topics: Statistics, Categorial data, Chi-square

Question: Given the null hypothesis that the opinion of college grads and non-grads is not different on the topic of drilling for oil and natural gas off the coast of California, conduct a chi-square test. Identify the p-value. Please round to 3 decimal places.
Answer: 0.003
Topics: Statistics, Categorial data, Chi-square
Context: The CSV file ucla_textbooks_f18.csv contains a sample of courses collected from UCLA from Fall 2018, and the corresponding textbook prices collected from the UCLA bookstore and also from Amazon.

Question: Given the null hypothesis that there is no difference in the average textbook price from the UCLA bookstore and from Amazon, conduct a hypothesis test. Identify the standard error associated with the price difference. Please round to 2 decimal places.
Answer: 1.63
Topics: Statistics, Numerical data, Standard error

Question: Given the null hypothesis that there is no difference in the average textbook price from the UCLA bookstore and from Amazon, conduct a hypothesis test. Identify the T-statistic of the price difference. Please round to 2 decimal places.
Answer: 2.20
Topics: Statistics, Numerical data, T-statistic

Question: Given the null hypothesis that there is no difference in the average textbook price from the UCLA bookstore and from Amazon, conduct a hypothesis test. Identify the p-value. Please round to 4 decimal places.
Answer: 0.0312
Topics: Statistics, Numerical data, P-value

Question: Create a 95% confidence interval for the average price difference between books at the UCLA bookstore and books on Amazon (price at the UCLA bookstore minus price on Amazon). Output the lower bound of the confidence interval. Please round to 2 decimal places.
Answer: 0.32
Topics: Statistics, Numerical data, Confidence interval
Context: The CSV file climate70.csv contains a random set of monitoring locations taken from NOAA data that had both years of interest (1948 and 2018) as well as data for both summary metrics of interest (dx70 and dx90, which are described below). It is a data frame with 197 observations on the following 7 variables.
Variable Description
station: Station ID.
latitude: Latitude of the station.
longitude: Longitude of the station.
dx70_1948: Number of days above 70 degrees in 1948.
dx70_2018: Number of days above 70 degrees in 2018.
dx90_1948: Number of days above 90 degrees in 1948.
dx90_2018: Number of days above 90 degrees in 2018.

Question: Please calculate the T-statistic of the hypothesis "There is no difference in average number of days exceeding 90°F in 1948 and 2018 for NOAA stations". Please round to the nearest hundredth.
Answer: 2.36
Topics: Statistics, Inference, Hypothesis validation, T-statistic

Question: Please calculate the p-value of the hypothesis "There is no difference in average number of days exceeding 90°F in 1948 and 2018 for NOAA stations". Please round to the nearest thousandth.
Answer: 0.019
Topics: Statistics, Inference, Hypothesis validation, P-value

Question: Calculate a 90% confidence interval for the average difference between the number of days exceeding 90°F between 1948 and 2018 (days in 2018 minus days in 1948). Output the upper bound of the confidence interval. Please round to 2 decimal places.
Answer: 4.93
Topics: Statistics, Inference, Confidence interval
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_2.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. L Central chest pain causes L shoulder impingement
B. L shoulder impingement causes L Central chest pain
C. No causal relationship exists
Please answer with A, B, or C.
Answer: C
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_9.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. backache causes L leg pain
B. L leg pain causes backache
C. No causal relationship exists
Please answer with A, B, or C.
Answer: C
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_10.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. R thumb trouble causes L Central chest pain
B. L Central chest pain causes R thumb trouble
C. No causal relationship exists
Please answer with A, B, or C.
Answer: C
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_16.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. L ham problem causes R ham problem
B. R ham problem causes L ham problem
C. No causal relationship exists
Please answer with A, B, or C.
Answer: C
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_19.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. L knee pain causes L lateral foot pain
B. L lateral foot pain causes L knee pain
C. No causal relationship exists
Please answer with A, B, or C.
Answer: C
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_22.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. R leg pain causes R medical obesity
B. R medical obesity causes R leg pain
C. No causal relationship exists
Please answer with A, B, or C.
Answer: C
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_25.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. R hip pain causes L L4 radiculopathy
B. L L4 radiculopathy causes R hip pain
C. No causal relationship exists
Please answer with A, B, or C.
Answer: B
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_28.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. L lateral arm discomfort causes R C6 radiculopathy
B. R C6 radiculopathy causes L lateral arm discomfort
C. No causal relationship exists
Please answer with A, B, or C.
Answer: B
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_30.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. IBS causes R L1 radiculopathy
B. R L1 radiculopathy causes IBS
C. No causal relationship exists
Please answer with A, B, or C.
Answer: B
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_33.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. L C6 radiculopathy causes R hand problems
B. R hand problems causes L C6 radiculopathy
C. No causal relationship exists
Please answer with A, B, or C.
Answer: A
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_34.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. L lateral obesity causes L L4 radiculopathy
B. L L4 radiculopathy causes L lateral obesity
C. No causal relationship exists
Please answer with A, B, or C.
Answer: B
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_37.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. R intracapular problems causes L C5 radiculopathy
B. L C5 radiculopathy causes R intracapular problems
C. No causal relationship exists
Please answer with A, B, or C.
Answer: B
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_42.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. R L5 radiculopathy causes L back headache
B. L back headache causes R L5 radiculopathy
C. No causal relationship exists
Please answer with A, B, or C.
Answer: A
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_43.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. R chest disorders causes L T5 radiculopathy
B. L T5 radiculopathy causes R chest disorders
C. No causal relationship exists
Please answer with A, B, or C.
Answer: B
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_46.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. R Lumbago causes L L4 radiculopathy
B. L L4 radiculopathy causes R Lumbago
C. No causal relationship exists
Please answer with A, B, or C.
Answer: B
Topics: Causality, Full graph causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_pairwise_1.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. L obesity causes R S1 radiculopathy
B. R S1 radiculopathy causes L obesity
Please answer with A or B.
Answer: B
Topics: Causality, Pairwise causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_pairwise_3.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. R L1 radiculopathy causes IBS
B. IBS causes R L1 radiculopathy
Please answer with A or B.
Answer: A
Topics: Causality, Pairwise causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_pairwise_5.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. toracal dysfunction causes L T6 radiculopathy
B. L T6 radiculopathy causes toracal dysfunction
Please answer with A or B.
Answer: B
Topics: Causality, Pairwise causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_pairwise_8.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. R medical obesity causes R L4 radiculopathy
B. R L4 radiculopathy causes R medical obesity
Please answer with A or B.
Answer: B
Topics: Causality, Pairwise causal discovery, Observational data
Context: The neuropathic pain diagnosis mainly consists of symptom diagnosis, pattern diagnosis, and pathophysiological diagnosis. The csv file Neuropathic_pairwise_28.csv contains neuropathic pain diagnosis records in the form of tables of which the row represents different patients and the column represents different diagnostic labels.

Question: Which cause-and-effect relationship is more likely?
A. toracal dysfunction causes R T4 radiculopathy
B. R T4 radiculopathy causes toracal dysfunction
Please answer with A or B.
Answer: B
Topics: Causality, Pairwise causal discovery, Observational data
Context: The CSV file ihdp_0.csv contains data obtained from the Infant Health and Development Program (IHDP). The study is designed to evaluate the effect of home visit from specialist doctors on the cognitive test scores of premature infants. The confounders x (x1-x25) correspond to collected measurements of the children and their mothers, including measurements on the child (birth weight, head circumference, weeks born preterm, birth order, first born, neonatal health index, sex, twin status), as well as behaviors engaged in during the pregnancy (smoked cigarettes, drank alcohol, took drugs) and measurements on the mother at the time she gave birth (age, marital status, educational attainment, whether she worked during pregnancy, whether she received prenatal care) and the site (8 total) in which the family resided at the start of the intervention. There are 6 continuous covariates and 19 binary covariates.

Question: What is the Average treatment effect of the dataset? Please round to the nearest hundredth.
Answer: 4.02
Topics: Causality, Average treatment effect, Observational data

Question: What is the Average treatment effect on the treated of the dataset? Please round to the nearest hundredth.
Answer: 4.00
Topics: Causality, Average treatment effect on the treated, Observational data

Question: What is the Average treatment effect on the control of the dataset? Please round to the nearest hundredth.
Answer: 4.02
Topics: Causality, Average treatment effect on the control, Observational data