I am always cautious when analyzing a data stream where there is no norm to work with (Without any outside factors and all things being equal one would assume that, in the long run, that they would be split 50/50).. How can you tell the difference between random/accidental data falling in conjunction as opposed to causal inference. Next, we have to begin by understanding the data's context—acknowledge its origin, biases, and external influences as they relate to the data. I would love to see more of your analysis. This is way outside my field so excuse my ignorance! My statistical analysis duties consist of subjects far less interesting such as Production Engineering and Manufacturing Process Engineering.
Brilliant. Seth Keshel, has also done this analysis. Some of us knew without the "data" since it happened mid night after DJT was winning "hugely". Evidence is quite franky more academic than useful.
I am always cautious when analyzing a data stream where there is no norm to work with (Without any outside factors and all things being equal one would assume that, in the long run, that they would be split 50/50).. How can you tell the difference between random/accidental data falling in conjunction as opposed to causal inference. Next, we have to begin by understanding the data's context—acknowledge its origin, biases, and external influences as they relate to the data. I would love to see more of your analysis. This is way outside my field so excuse my ignorance! My statistical analysis duties consist of subjects far less interesting such as Production Engineering and Manufacturing Process Engineering.
Brilliant. Seth Keshel, has also done this analysis. Some of us knew without the "data" since it happened mid night after DJT was winning "hugely". Evidence is quite franky more academic than useful.