For example, let’s imagine that melatonin is effective for 60% of all people: 80% of people who describe themselves as “morning people”, but only 40% of people who do not. This is useful melaton
I think that an important addition would be other data about the participants in a given intervention, that could ideally help newcomers filter out interventions which are reasonably likely to have a positive effect in the general population but unlikely to apply to some subset of people.I think that an important addition would be other data about the participants in a given intervention, that could ideally help newcomers filter out interventions which are reasonably likely to have a positive effect in the general population but unlikely to apply to some subset of people.I think that an important addition would be other data about the participants in a given intervention, that could ideally help newcomers filter out interventions which are reasonably likely to have a positive effect in the general population but unlikely to apply to some subset of people.I think that an important addition would be other data about the participants in a given intervention, that could ideally help newcomers filter out interventions which are reasonably likely to have a positive effect in the general population but unlikely to apply to some subset of people.
For example, let’s imagine that melatonin is effective for 60% of all people: 80% of people who describe themselves as “morning people”, but only 40% of people who do not. This is useful information for both groups (assuming the difference is statistically significant), and would be lovely to include in our cookbook.
This would require more information-gathering about individual users (and we should definitely have a “decline to disclose” option, particularly for more sensitive topics). If we want to be able to change what data we are collecting later (imagine that we suddenly have reason to believe that hair color is relevant to melatonin impact), we will need to store individual usernames in order to contact participants later.
I would be interested in helping with this project. My employer currently owns anything software-related I produce, but is willing to make reasonable exceptions where a project does not intersect with its business; if this project does materialize in a more concrete form, I would be able to present it to my employer and ask for permission to contribute. So if someone starts it, I would like to support it.
For example, let’s imagine that melatonin is effective for 60% of all people: 80% of people who describe themselves as “morning people”, but only 40% of people who do not. This is useful information for both groups (assuming the difference is statistically significant), and would be lovely to include in our cookbook.
This would require more information-gathering about individual users (and we should definitely have a “decline to disclose” option, particularly for more sensitive topics). If we want to be able to change what data we are collecting later (imagine that we suddenly have reason to believe that hair color is relevant to melatonin impact), we will need to store individual usernames in order to contact participants later.
I apologize for the formatting; I tried to copy and paste from another app to get around the character-eating behavior of the comment box on mobile, and it seems to have resulted in this monstrosity which is immune to edits.
Perhaps I ought to just start posting comments as links to Google Docs.
For example, let’s imagine that melatonin is effective for 60% of all people: 80% of people who describe themselves as “morning people”, but only 40% of people who do not. This is useful melaton
I think that an important addition would be other data about the participants in a given intervention, that could ideally help newcomers filter out interventions which are reasonably likely to have a positive effect in the general population but unlikely to apply to some subset of people.I think that an important addition would be other data about the participants in a given intervention, that could ideally help newcomers filter out interventions which are reasonably likely to have a positive effect in the general population but unlikely to apply to some subset of people.I think that an important addition would be other data about the participants in a given intervention, that could ideally help newcomers filter out interventions which are reasonably likely to have a positive effect in the general population but unlikely to apply to some subset of people.I think that an important addition would be other data about the participants in a given intervention, that could ideally help newcomers filter out interventions which are reasonably likely to have a positive effect in the general population but unlikely to apply to some subset of people.
For example, let’s imagine that melatonin is effective for 60% of all people: 80% of people who describe themselves as “morning people”, but only 40% of people who do not. This is useful information for both groups (assuming the difference is statistically significant), and would be lovely to include in our cookbook.
This would require more information-gathering about individual users (and we should definitely have a “decline to disclose” option, particularly for more sensitive topics). If we want to be able to change what data we are collecting later (imagine that we suddenly have reason to believe that hair color is relevant to melatonin impact), we will need to store individual usernames in order to contact participants later.
I would be interested in helping with this project. My employer currently owns anything software-related I produce, but is willing to make reasonable exceptions where a project does not intersect with its business; if this project does materialize in a more concrete form, I would be able to present it to my employer and ask for permission to contribute. So if someone starts it, I would like to support it.
For example, let’s imagine that melatonin is effective for 60% of all people: 80% of people who describe themselves as “morning people”, but only 40% of people who do not. This is useful information for both groups (assuming the difference is statistically significant), and would be lovely to include in our cookbook.
This would require more information-gathering about individual users (and we should definitely have a “decline to disclose” option, particularly for more sensitive topics). If we want to be able to change what data we are collecting later (imagine that we suddenly have reason to believe that hair color is relevant to melatonin impact), we will need to store individual usernames in order to contact participants later.
I apologize for the formatting; I tried to copy and paste from another app to get around the character-eating behavior of the comment box on mobile, and it seems to have resulted in this monstrosity which is immune to edits.
Perhaps I ought to just start posting comments as links to Google Docs.