Relating Models to Data Lecture 14 all images are screenshots from the respective applications add slides on e-cell, computational physics (n-body)

Why Relate Models to Data? Theory Phenomena ? informs produces Scientific models reflect theoretical knowledge, whereas data reflect phenomena. Models that accurately account for data forge an evidentiary link between theory and phenomena. Traditionally, hypothesis testing has formed the connection between models and data. However, informatics tools can expand scientists’ ability to relate models and data.

The Hypothetico-Deductive View Recall the hypothetico–deductive view of science. The comparison stage often includes one or more statistical tests, but it may also involve other, more intuitive evaluations. hypotheses deduction predictions observations comparison evaluation

Traditional Hypothesis Testing In statistics, hypothesis testing requires scientists to select: a null hypothesis (e.g., mean < 0), an alternative hypothesis (e.g., mean ≥ 0), an appropriate test (e.g., paired t test), and a level of significance (e.g., 0.05). The results of the test are compared to the level of significance and one of the hypotheses is accepted. Informatics tools can easily apply statistical tests and provide reports to scientists. The primary source of confusion lies in selecting the statistical test and in ensuring that its assumptions are met.

R R is one of several informatics systems that support statistical hypothesis testing. Scientists interact with R through a programming language specialized for statistical computing.

Alternative Ways to Relate Models to Data Informatics tools discussed in earlier lectures offer other approaches to relating models to data. These may complement and supplant statistical analyses. The simplest approach lets scientists simultaneously view measurements and simulation results as in Prometheus.

Data and Models in RockWorks RockWorks lets researchers view GIS data and borehole data along with the stratigraphic model. Scientists can quickly see different models for spatial interpolation reflect their observations.

Relating Data and Models in Chimera Chimera lets researchers align models of proteins with data from an electron microscope. The software provides a tool that fits molecular models into electron density maps based on local search algorithms.

Relating Qualitative Models to Data Hybrow relates models to data by identifying facts that contradict the model and suggesting revisions. other model checking approaches might apply (Fisher and Schaub). add these in the next iteration. Hybrow reflects a falsificationist (Popperian) view of science and reports contradictions.

Relating Qualitative Models to Data GenePath identifies and applies revisions to the model, but lets scientists see the influence of different data sources. Ignoring data can suggest structural changes or no change at all.

Anomalies in Science Tools such as Hybrow locate data and findings that are anomalous to a given model. This contrasts with the approaches of confirmatory statistics, which say little about anomalies. This capability is important since anomalies drive scientific discovery, encouraging the revision of models and theories. Informatics tools should provide methods to identify and respond to data that violate a model’s predictions.

Summary Informatics systems have made comparing models to data readily accessible to scientists. Software such as R lets researchers load their data sets from a file and carry out routine statistical tests. Other programs enable the simultaneous visualization of model simulations and measurements. Still others overlay data and models and offer domain specific approaches for comparison. These tools let scientists explore intuitive notions of scientific evidence and view anomalies within their context.