Experiment Dataset Structure
When you run an experiment, such as an A/B test, the dataset you collect follows a typical structure. Each row in the dataset usually represents a single user, session, or observation. The columns capture important attributes, which often include:
- A unique identifier for each observation, such as
user_idorsession_id; - A
grouplabel that indicates whether the observation is part of the control or treatment group; - One or more metric columns, such as
conversion,revenue,clicks, or another key outcome variable; - Additional attributes, like
timestamp,device_type, orcountry, which can help with deeper analysis or segmentation.
Data types are important for correct analysis. Identifiers are typically stored as strings or integers. Group labels are often categorical (such as 'control' or 'treatment'). Metric columns may be numeric (integers or floats), and other columns may be categorical or datetime types. This structure allows you to easily group, filter, and analyze your results by segment or group.
12345678910111213import pandas as pd # Create a sample experiment dataset data = { "user_id": [101, 102, 103, 104, 105, 106], "group": ["control", "treatment", "control", "treatment", "control", "treatment"], "conversion": [0, 1, 1, 0, 0, 1], "revenue": [0.00, 10.50, 5.75, 0.00, 0.00, 12.00], "device_type": ["mobile", "desktop", "desktop", "mobile", "mobile", "desktop"] } df = pd.DataFrame(data) print(df)
A well-structured experiment dataset makes your analysis both reliable and efficient. With clear group labels, you can easily compare results between control and treatment groups. Numeric metric columns allow for direct calculation of averages, variances, and statistical tests. Categorical and timestamp columns enable deeper segmentation and trend analysis. This organization also simplifies data cleaning, balance checks, and metric construction—key steps before running any statistical tests.
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When you run an experiment, such as an A/B test, the dataset you collect follows a typical structure. Each row in the dataset usually represents a single user, session, or observation. The columns capture important attributes, which often include:
- A unique identifier for each observation, such as
user_idorsession_id; - A
grouplabel that indicates whether the observation is part of the control or treatment group; - One or more metric columns, such as
conversion,revenue,clicks, or another key outcome variable; - Additional attributes, like
timestamp,device_type, orcountry, which can help with deeper analysis or segmentation.
Data types are important for correct analysis. Identifiers are typically stored as strings or integers. Group labels are often categorical (such as 'control' or 'treatment'). Metric columns may be numeric (integers or floats), and other columns may be categorical or datetime types. This structure allows you to easily group, filter, and analyze your results by segment or group.
12345678910111213import pandas as pd # Create a sample experiment dataset data = { "user_id": [101, 102, 103, 104, 105, 106], "group": ["control", "treatment", "control", "treatment", "control", "treatment"], "conversion": [0, 1, 1, 0, 0, 1], "revenue": [0.00, 10.50, 5.75, 0.00, 0.00, 12.00], "device_type": ["mobile", "desktop", "desktop", "mobile", "mobile", "desktop"] } df = pd.DataFrame(data) print(df)
A well-structured experiment dataset makes your analysis both reliable and efficient. With clear group labels, you can easily compare results between control and treatment groups. Numeric metric columns allow for direct calculation of averages, variances, and statistical tests. Categorical and timestamp columns enable deeper segmentation and trend analysis. This organization also simplifies data cleaning, balance checks, and metric construction—key steps before running any statistical tests.
Merci pour vos commentaires !
