Alternative interpretation: an alternative interpretation is an interpretation that can replace another interpretation or set of interpretations. It has different results for the model. If more than 1 interpretation is possible for something, the interpretations should be considered “alternative” for each other. Alternative interpretations should be considered equally valid until facts have proven otherwise. In all cases, as long as alternative interpretations are possible, depending conclusions should be considered uncertain.
Assumption: an assumption is a proposed statement that temporarily fills in an uncertainty for research purposes. Assumptions can be useful to construct models, but should be used with care. An assumption adds an uncertainty to the model and can easily lead to tunnel vision or circular reasoning.
Circular reasoning: circular reasoning occurs when a statement becomes its own support. (Using circular reasoning as a fact is just as impossible as pulling yourself up by pulling your own hair.)
Compatibility: (sub)models are compatible when they do not have contradictions. A combination of small compatible models form a larger model. Conclusions from one model can only falsify conclusions from another model if the models are compatible.
Conclusion: a conclusion is, if done properly, a true statement. It is based on a set of facts and interpretations. In case a conclusion is based on facts only, it can be used like a fact. One should always realize that the conclusion is always as weak as the weakest links of its support. A conclusion can never support itself, not even indirectly. In a case where a conclusion supports itself, it must be considered circular reasoning. It is an uncertainty that cannot be used like a fact. A conclusion that includes assumptions or circular reasoning is equal to an assumption.
Conditional fact: a conditional fact is a fact that is only true if it satisfies its required conditions. A conditional fact can always be defined as an if-then statement.
Experiment: An experiment is a deliberate action with the purpose to test a hypothesis or prediction or to answer a question.
Fact: a fact is an observed event or object, a measurement from a device or a physical object.
Fiction: fiction is information that describes fantasy in stead of reality. Therefore fiction is not true. In most cases, the purpose of fiction is amusement. In some cases, fiction is based on fantasy by error. In those cases, the writer misinterpreted fantasy as reality. In some cases, fiction is a lie: it is deliberate false information with the purpose to deceive others. In any case where information is suggested to be fiction, this must be verified. In most cases, it is easy to verify the purpose of the information. If the purpose of the information appears to be to describe reality, the information should be investigated as such, and can only be considered fiction if it is proven that the source of information is unreliable. Even if the information is fiction, parts of the information can be true.
Fiction with the purpose of amusement is relatively modern. Therefore one should be careful to consider very old sources fiction. (Ancient) myths are usually not meant to be fiction by the writer.
Hypothesis: a hypothesis is a suggested explanation with the purpose of testing. Therefore a hypothesis must be falsifiable. An unfalsifiable hypothesis is a purposeless assumption that can significantly obstruct truth research and hinder development in science. An example of an unfalsifiable hypothesis is evolution. Another example of an unfalsifiable hypothesis is supernaturality without evidence.
Interpretation: an interpretation is a proposed explanation for a fact.
Model: a model is a set of facts and interpretation to provide a framework. It is useful for organizing data and making predictions. A model can be compared with a puzzle, facts can be compared with pieces of a puzzle and interpretations can be compared with pieces of a puzzle linked together.
There are various types of models. For example, some models are meant for numerical analysis of data or simulations. Other models are meant for scenario analysis. Numerical analysis tend to be more absolute and clear. Scenario analysis models are usually much more pragmatic. It depends on the purpose of the model which model type is more useful. In a lot of cases, a combination of the model types can be used. Scenario analysis models can be considered forensic science.
Myth: basically, myth is a synonym for story. Myth is not a synonym for fiction.
Opinion: opinions are the personal preferences of a person. Opinions are therefore irrelevant for research purposes. Statements like “I like it”, “I don’t like it”, “we do not want this”, “absurd”, “too simple”, “unwanted” etc. are all opinions and thus irrelevant. Opinions can be helpful in cases of intuition, and dangerous in cases of cognitive dissonance. Opinions can influence the priority of what to research. However, it should never be leading in what to consider true.
Perspective: physical perspective is the view of an observer. It depends on the position of the observer and the view direction of that observer. Scientific perspective is the view of the researcher. It is dependent on the available knowledge of the scientist and on the personal view “direction” of the scientist.
Prediction: a prediction is an expected observation, following from a model
Straw man argument: if one attempts to falsify a model with a conclusion from another model that is incompatible with it, this is called a “straw man argument”. In that case the falsification is invalid and the attempted falsification has no effect.
Submodel: a submodel is a small model that can be (or is) integrated in a larger model. If a model is false, then its submodels could still be valid. It is possible to build large models out of multiple submodels, or to derive submodels out of large models. This can be compared to a puzzle. When a puzzle (model) is incorrect, large parts of the puzzle (submodels) can still be correct.
Truth: the truth is anything that is real.
Tunnel vision: tunnel vision is a narrow view perspective of the scientist. In case tunnel vision occurs, it can easily lead to cognitive dissonance as the scientist is no longer open to alternatives. Tunnel vision is therefore a risk within science. Tunnel vision slows down the investigation significantly, and can lead to false conclusions that are hard to detect. Tunnel vision is a psychological effect that should be avoided.