Scientific evidenceScientific evidence is evidence that serves to either support or counter a scientific theory or hypothesis, although scientists also use evidence in other ways, such as when applying theories to practical problems. Such evidence is expected to be empirical evidence and interpretable in accordance with the scientific method. Standards for scientific evidence vary according to the field of inquiry, but the strength of scientific evidence is generally based on the results of statistical analysis and the strength of scientific controls.
Theoretical physicsTheoretical physics is a branch of physics that employs mathematical models and abstractions of physical objects and systems to rationalize, explain and predict natural phenomena. This is in contrast to experimental physics, which uses experimental tools to probe these phenomena. The advancement of science generally depends on the interplay between experimental studies and theory. In some cases, theoretical physics adheres to standards of mathematical rigour while giving little weight to experiments and observations.
Circumstantial evidenceCircumstantial evidence is evidence that relies on an inference to connect it to a conclusion of fact—such as a fingerprint at the scene of a crime. By contrast, direct evidence supports the truth of an assertion directly—i.e., without need for any additional evidence or inference. On its own, circumstantial evidence allows for more than one explanation. Different pieces of circumstantial evidence may be required, so that each corroborates the conclusions drawn from the others.
ProductivityProductivity is the efficiency of production of goods or services expressed by some measure. Measurements of productivity are often expressed as a ratio of an aggregate output to a single input or an aggregate input used in a production process, i.e. output per unit of input, typically over a specific period of time. The most common example is the (aggregate) labour productivity measure, one example of which is GDP per worker.
TheoryA theory is a rational type of abstract thinking about a phenomenon, or the results of such thinking. The process of contemplative and rational thinking is often associated with such processes as observational study or research. Theories may be scientific, belong to a non-scientific discipline, or no discipline at all. Depending on the context, a theory's assertions might, for example, include generalized explanations of how nature works. The word has its roots in ancient Greek, but in modern use it has taken on several related meanings.
Trade agreementA trade agreement (also known as trade pact) is a wide-ranging taxes, tariff and trade treaty that often includes investment guarantees. It exists when two or more countries agree on terms that help them trade with each other. The most common trade agreements are of the preferential and free trade types, which are concluded in order to reduce (or eliminate) tariffs, quotas and other trade restrictions on items traded between the signatories.
Free trade agreementA free-trade agreement (FTA) or treaty is an agreement according to international law to form a free-trade area between the cooperating states. There are two types of trade agreements: bilateral and multilateral. Bilateral trade agreements occur when two countries agree to loosen trade restrictions between the two of them, generally to expand business opportunities. Multilateral trade agreements are agreements among three or more countries, and are the most difficult to negotiate and agree.
Transition economyA transition economy or transitional economy is an economy which is changing from a centrally planned economy to a market economy. Transition economies undergo a set of structural transformations intended to develop market-based institutions. These include economic liberalization, where prices are set by market forces rather than by a central planning organization.
Marginal productIn economics and in particular neoclassical economics, the marginal product or marginal physical productivity of an input (factor of production) is the change in output resulting from employing one more unit of a particular input (for instance, the change in output when a firm's labor is increased from five to six units), assuming that the quantities of other inputs are kept constant. The marginal product of a given input can be expressed as: where is the change in the firm's use of the input (conventionally a one-unit change) and is the change in quantity of output produced (resulting from the change in the input).
Big dataBig data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with a large body of information that we could not comprehend when used only in smaller amounts.
UnderemploymentUnderemployment is the underuse of a worker because a job does not use the worker's skills, is part-time, or leaves the worker idle. Examples include holding a part-time job despite desiring full-time work, and overqualification, in which the employee has education, experience, or skills beyond the requirements of the job. Underemployment has been studied from a variety of perspectives, including economics, management, psychology, and sociology. In economics, for example, the term underemployment has three different distinct meanings and applications.
Empirical risk minimizationEmpirical risk minimization (ERM) is a principle in statistical learning theory which defines a family of learning algorithms and is used to give theoretical bounds on their performance. The core idea is that we cannot know exactly how well an algorithm will work in practice (the true "risk") because we don't know the true distribution of data that the algorithm will work on, but we can instead measure its performance on a known set of training data (the "empirical" risk).
