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"hypothesis generation" is a correct and usable phrase in written English.
You can use it when you want to describe the process of forming hypotheses or potential explanations for a phenomenon. For example, "Now that the data has been collected, it is time to begin hypothesis generation."
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Further, this hypothesis generation itself generates discoveries that might lead to their own hypotheses.
The result is hypothesis generation.
Another approach was to utilize epidemiological data for hypothesis generation.
Finn called his approach to hypothesis generation the "JSM Method of Automatic Hypothesis Generation" (so named for similarities to John Stuart Mill's methods for identifying causes).
Instead, hypothesis generation must be inductively based, and (dis confirmation is a holistic matter.
Hypothesis generation is the creative part of science, while confirmation is the logical part.
Hypothesis generation is one of the most useful features in DESMSCI.
A second goal is to provide a tool that leads to useful hypothesis generation.
An illustrative simple example is shown in Fig. 9. Fig. 9 Hypothesis generation.
During a hypothesis generation run, it attempts to minimize a score function consisting of two terms.
Herein we will test this hypothesis through in silico experimentation and model based hypothesis generation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com