The phrase "and error to develop" is not correct and does not convey a clear meaning in written English.
It seems to be an incomplete thought and lacks context, making it difficult to determine when it could be used.
Example: "The process requires trial and error to develop effective solutions."
Alternatives: "and mistakes to learn" or "and experimentation to grow".
The protocols for them took years of aggressive trial and error to develop, with hundreds of patients suffering immensely, and yet scientists still do not fully understand how the body accepts foreign grafts.
Instead, they can now work things out for themselves, proceeding by trial and error to develop their own programs according to their needs – in what Ford calls (I don't think metaphorically) "a process of Darwinian natural selection".
This was to enable the researcher to establish causes and origins of such errors to develop a means of eliminating displayed errors.
This study recommends the strategy of focusing on elimination of errors to develop students' understanding of derivatives of trigonometric functions.
Herein, variance decomposition using the law of total variance is applied to previously published probabilistic models to explore the relative contributions of various sources of random error and to develop tools to aid experimental design.
This may be an important part of academic development, as it helps authors to identify potential errors and to develop alternative interpretations of their results before proceeding to submission for full publication.
This study aimed to provide an overview of frequently made errors and to develop easy-to-use checklists to verify the quality of such studies.
However, due to limited understanding of the molecular structure of antibody binding sites and antigenic epitopes, as well as the intermolecular binding forces that come into play, the traditional 'trial and error' method used to develop antibodies still remains the method of choice.
In conclusion, we reviewed error reports to identify patterns of CPOE-related errors and used them to develop a new taxonomy and recurring error scenarios.
The best correlation, which has least errors, is selected and subjected to develop non-linear correlations to decrease its estimation errors.
Toward the end, we design a new l1-norm distance metric for measuring misclassification errors and use it to develop a mixed 0 1 integer and linear program (MILP) for the k piecewise linear and convex separation of data.
