INDUCTIVE TESTING

Inductive reasoning is an important part scientific decision making.

DEFINITION: Inductive reasoning constructs arguments that are "knowledge expanding." Knowledge expanding means that conclusions of arguments exceed the sphere of the premises(Okasha, 2016). In experimental science, inductive reasoning typically involves generalizing based on a set of observations (although other forms of induction are possible; Moore and Parker, 2017).

Inductive reasoning has long been a part of scientific inquiry (Bacon, 1620). However, critics of have questioned whether inductive reasoning should be used to defend scientific conclusions (Popper, 1959). Inductive reasoning is not truth preserving, and inductive arguments cannot be "valid" or "sound." Therefore, inductive reasoning cannot lead to definitive conclusions like valid deductive arguments can.

Nevertheless, inductive reasoning continues to be used widely in science (Okasha, 2016). Inductive reasoning is important for several reasons:

(1) First, scientists, engineers, policy-makers and others often must make evidence-based decisions based on existing information. It may not be possible for decision-makers to wait for critical experiments to reject alternative hypotheses and deductively converge on strongly-supported models through a process such as Strong Inference. For example, inductive reasoning based on existing information overwhelming supports the hypothesis that human activities are causing rapid climate change. Human-caused climate change will cause substantial social and economic disruption in coming years (Ripple et al., 2019). The most reasonable conclusion is to take action to prevent additional climate change now, and not wait until 'untold' human suffering validates current scientific predictions.

(2) Second, it may simply not be possible to enumerate and clearly test all necessary alternative hypotheses (O'Donohue and Buchanan, 2001). Scientists may not have the required conceptual or experimental frameworks that can discriminate between alternatives. For example, it is not possible to perform direct experiments in the past. Evolutionary biologists, anthropologists, and others may use inductive reasoning to support explanations that are most consistent with available information.

(3) Third, strong deductive reasoning is often only useful in very specific circumstances, such as testing statistical null hypotheses. Interpreting the results of testing hypotheses often requires inductive judgments. For example, deciding whether rejecting a statistical null hypothesis is sufficient evidence to support a research hypothesis may involve consideration of the size and type of differences among groups in additional to the outcome of the statistical tests. Inductive reasoning may be necessary to weigh different types of evidence to determine the most reasonable conclusions. Moreover, creating general, measurable, and statistical hypotheses often involves making assumptions. Testing measurable and general hypotheses often requires inductive reasoning that accounts from the results of many statistical tests, or many experiments.

Science may benefit from a diversity of approaches (O'Donohue and Buchanan, 2001). Scientific discoveries may sometimes emerge from departures from the hypothetico-deductive system exemplified by Strong Inference. For example, the discovery of penicillin that revolutionized medicine resulted from induction based on a chance observation (Lobanovska and Pilla, 2017). Therefore, inductive approaches are an important part of science.

Inductive reasoning can also help to clarify the logic and presentation of individual studies. Three specific uses for Inductive reasoning are to:

1) Generate General Hypotheses to test deductively.

2) Resolve conflicts among data and weigh evidence to defend results.

3) Support General Hypotheses that no single experiment can test.

The Introduction and Discussion sections will explain each of the three specific uses for inductive reasoning in more detail. In both the Introduction and the Discussion, using specific frameworks can help to structure inductive reasoning.