Science - Reliability & Validity


Scientific research produces results, which the scientists analyse and report  to others (usually in science journals).  These results are assessed in terms of how reliable they are AND how valid they are.  The goal is to have findings that are BOTH.


In science, reliability has to do with replication.  The more something is replicated in research, the more reliable it is.  Another way of putting it is, reliability results when the same (or highly compatible) results are achieved by same study repeated either over time, or by different researchers, or both.

Types of Reliability: There are several ways to determine how reliable research findings are.  They focus on the means for obtaining data (e.g., how things are measured).  So most discussions of reliability are directed at tests that purportedly measure something (in the case of climate, this would involve things like the reliability of the thermometers used to measure temperature).  Here are the types of reliability:


Loosely speaking, this refers to truth (i.e., how true are the findings of the research).  There are several different types of validity.

Content/Construct Validity:

What this looks at is does the research actually study what it says it studied (e.g., Does an IQ test really measure intelligence)?  Were the constructs (these are theoretical concepts) and the contents (design of the study) appropriate to the study?  In other words, doing an experiment about subatomic particles by weighing baseballs is NOT valid.

Internal Validity:

Internal validity occurs when it can be concluded that there is a causal relationship between the variables being studied. A danger is that changes might be caused by other factors.  In other words, internal validity means that we have looked at and controlled enough of the variable, both causes and effects, to say that A caused B.  When internal validity is strong, successful replication of the study is very likely (in other words, it increases the reliability of the findings).

It is important to note that experimental studies examine causal relationships, but correlational studies do not--they just look at how two, or more things may change together.  When multi-variant (i.e., multicorrelational) studies occur, the correlations that arise have a higher probability of revealing possible causality.

External Validity:

This occurs when the finding in the study can be generalized to the world at large.  In other words, the findings aren't limited to the laboratory.

Predictive Validity:

Some might consider this the ultimate goal of science, predicting what can and will happen.  Science seeks to know what causes will lead to what effects in what circumstances.  As mathematic models (often employing probability and a new notion called "fuzzy logic") have become more complex and sophisticated, science looks toward the interaction of multiple causes leading to multiple effects.  A prime example of this would be weather prediction (we're talking about local daily weather, not climate, but it can extend into that area as well).

Scientist consider their research and the theories they use valid when it leads to prediction (e.g., predicting what will happen if dynamite is ignited.  More importantly, prediction can lead to control.  If we can predict an outcome from certain causes, we can possibly create the outcome when we want and need it to happen, OR we may be able to prevent that outcomes by eliminating the causes.

NOTE: One reasons climatologists see climate change research (e.g., the global warming) to be valid is the fact that it was predicted several decades ago.  And the fact that it was predicted well before any possible financial gain also addresses the issue of motive that has led some to assert the only reason scientists support global warming is in hopes of getting rich off it.

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