science

Why–because analysis

Why–because analysis is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Why–because analysis rather than just read about it. In short: Why–because analysis (WBA) is a method for accident analysis using graph theory. It is independent of application domain and has been used to analyse, among others, aviation-, railway-, marine-, and computer-related accidents and incidents.

Key takeaways

Why–because analysis belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Why–because analysis to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Why–because analysis from memory before moving on to harder problems.

Reference excerpt

Why–because analysis (WBA) is a method for accident analysis using graph theory. It is independent of application domain and has been used to analyse, among others, aviation-, railway-, marine-, and computer-related accidents and incidents. It is mainly used as an after-the-fact (or a posteriori) analysis method, and it is applied to ensure that results are objective, falsifiable and reproducible. The result of such an analysis is a why–because graph (WBG), a type of causal notation used to represent interdependencies within a system and depict causal relations between factors of an accident. It is a directed acyclic graph, the nodes of which are factors. Directed edges denote cause–effect relations between the factors. Why–because analyses start with questions regarding the nature of an accident (which is easy to define in many cases), followed by an iterative process to determine causes. When causes for an accident have been identified, formal tests are applied to all potential cause-effect relations. This process can be iterated for newfound causes, and so on, until a satisfactory result has been achieved. At each node (factor), each contributing cause (related factor) must have been necessary to cause the accident, and the totality of causes must have been sufficient to do so.

Formal tests

A counterfactual test (CT) leads back to David Lewis' formal notion of causality and counterfactuals. This test determines whether an effect would have happened without a particular cause, and proves or disproves that a cause is a necessary causal factor for an effect. A clear link between a cause and an effect can only be established if that effect cannot occur without that cause.

C happens, then E happens. If E cannot occur unless C happens first, then C is a necessary causal factor for E and cannot be ruled out. If E can occur without C happening first, then C is not necessary for E to occur. A causal sufficiency test (CST) determines whether the occurrence of all of the attributed causes together is guaranteed to result in a given effect. It aims at deciding whether a set of causes are sufficient for an effect to happen. The missing causes can thus be identified.

C happens, then E happens. If C always results in E, then C is a sufficient causal factor for E, and other causal factors are not necessary. If C does not always result in E, then C is insufficient for E to occur. A why–because graph can be correct only if the causal sufficiency test is positive for all causal relations and for all sets of causes to their effects:

Each cause must be necessary (CT) The totality of causes must be sufficient (CST) Nothing is omitted (CST: the listed causes are sufficient) Nothing is superfluous (CT: each cause is necessary)

See also Accident Cause–effect graph Fault tree analysis Five whys Ishikawa diagram Issue map Issue tree Root cause analysis

References

External links Why-Because Analysis (WBA)

Illustrations

Worked examples

Example 1 — a first encounter with Why–because analysis

Start with the simplest possible case. Write down what Why–because analysis claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Why–because analysis before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Why–because analysis ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Why–because analysis

In research: Why–because analysis appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Why–because analysis in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Why–because analysis is common in secondary-school and first-year university syllabi. It links to neighbouring topics Accident analysis, Causal diagrams, Debugging, so understanding it makes those chapters shorter.
In everyday life: Look for Why–because analysis outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Why–because analysis in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Why–because analysis means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Why–because analysis out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Why–because analysis in simple terms?

Why does Why–because analysis matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Why–because analysis?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Why–because analysis.