Binary vs. continuous data outcomes

In summary, binary data outcomes only have two possible outcomes, while continuous data outcomes have an infinite number of possible values. Continuous data is typically used in statistical analyses for its precision and detail. Examples of binary data outcomes include yes/no responses, true/false statements, and presence/absence of a certain characteristic. Binary and continuous data outcomes can be used together in certain analyses, such as logistic regression. The type of data to collect for a research study depends on the research question and variables being studied, with binary data being suitable for categorical variables and continuous data for numerical variables.
  • #1
Loren Booda
3,125
4
On the average, which provides more information: yes/no answers or answers neither yes nor no?

Just asking.
 
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  • #3
In other words, is a random outcome more likely to be 0 or 1, or between 0 and 1?
 

What is the difference between binary and continuous data outcomes?

Binary data outcomes only have two possible outcomes, usually represented as 0 or 1. Continuous data outcomes have an infinite number of possible values between a minimum and maximum value.

Which type of data is typically used in statistical analyses?

Continuous data is typically used in statistical analyses because it allows for more precise measurements and can provide a more detailed understanding of the data.

What are some examples of binary data outcomes?

Examples of binary data outcomes include yes or no responses, true or false statements, and presence or absence of a certain characteristic.

Can binary and continuous data outcomes be used together in the same analysis?

Yes, they can be used together in certain types of analyses. For example, logistic regression can be used to analyze the relationship between a continuous predictor variable and a binary outcome variable.

How do you choose which type of data to collect for a research study?

The type of data to collect depends on the research question and the variables being studied. If the variables are categorical or have clear yes/no outcomes, then binary data may be appropriate. If the variables are numerical and can have a range of values, then continuous data may be more suitable.

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