◥ University. {q} PhD. {tr} Training. {R} Method. ☢️ Hypothesis. ☢️ Text. ☢️ Benchmarks. ☢️ Fact. ◇ POPPER, Karl. ◇ KUHN, Thomas. Scientific method. Diagram illustrating steps in the scientific method. The scientific method is an ongoing process, which usually begins with observations about the natural world. Human beings are naturally inquisitive, so they often come up with questions about things they see or hear and often develop ideas (hypotheses) about why things are the way they are. The best hypotheses lead to predictions that can be tested in various ways, including making further observations about nature.
In general, the strongest tests of hypotheses come from carefully controlled and replicated experiments that gather empirical data. Although procedures vary from one field of inquiry to another, identifiable features are frequently shared in common between them. Overview The DNA example below is a synopsis of this method Process Formulation of a question The question can refer to the explanation of a specific observation, as in "Why is the sky blue?
" Hypothesis Prediction Testing Analysis DNA example Other components Replication 1. Steps of the Scientific Method. Please ensure you have JavaScript enabled in your browser. If you leave JavaScript disabled, you will only access a portion of the content we are providing. <a href="/science-fair-projects/javascript_help.php">Here's how. </a> What is the Scientific Method? The scientific method is a process for experimentation that is used to explore observations and answer questions. Does this mean all scientists follow exactly this process? No. Even though we show the scientific method as a series of steps, keep in mind that new information or thinking might cause a scientist to back up and repeat steps at any point during the process. Whether you are doing a science fair project, a classroom science activity, independent research, or any other hands-on science inquiry understanding the steps of the scientific method will help you focus your scientific question and work through your observations and data to answer the question as well as possible.
Educator Tools for Teaching the Scientific Method. ☢️ Variable. Mechanism. From Wikipedia, the free encyclopedia Mechanism may refer to: Level of measurement. In statistics and quantitative research methodology, various attempts have been made to classify variables (or types of data) and thereby develop a taxonomy of levels of measurement or scales of measure. Perhaps the best known are those developed by the psychologist Stanley Smith Stevens. He proposed four types: nominal, ordinal, interval, and ratio. Typology[edit] Nominal scale[edit] The nominal type, sometimes also called the qualitative type, differentiates between items or subjects based only on their names or (meta-)categories and other qualitative classifications they belong to; thus dichotomous data involves the construction of classifications as well as the classification of items. Central tendency[edit] Ordinal scale[edit] The ordinal type allows for rank order (1st, 2nd, 3rd, etc.) by which data can be sorted, but still does not allow for relative degree of difference between them.
Central tendency[edit] Interval scale[edit] Central tendency and statistical dispersion[edit] L. Theory. Occam's razor. The sun, moon and other solar system planets can be described as revolving around the Earth. However that explanation's ideological and complex assumptions are completely unfounded compared to the modern consensus that all solar system planets revolve around the Sun. Ockham's razor (also written as Occam's razor and in Latin lex parsimoniae) is a principle of parsimony, economy, or succinctness used in problem-solving devised by William of Ockham (c. 1287 - 1347). It states that among competing hypotheses, the one with the fewest assumptions should be selected.
Other, more complicated solutions may ultimately prove correct, but—in the absence of certainty—the fewer assumptions that are made, the better. Solomonoff's theory of inductive inference is a mathematically formalized Occam's Razor:[2][3][4][5][6][7] shorter computable theories have more weight when calculating the probability of the next observation, using all computable theories which perfectly describe previous observations. Theory choice. A main problem in the philosophy of science in the early 20th century, and under the impact of the new and controversial theories of relativity and quantum physics, came to involve how scientists should choose between competing theories. The classical answer would be to select the theory which was best verified, against which Karl Popper argued that competing theories should be subjected to comparative tests and the one chosen which survived the tests.
If two theories could not, for practical reasons, be tested one should prefer the one with the highest degree of empirical content, said Popper in The Logic of Scientific Discovery. Mathematician and physicist Henri Poincaré instead, like many others, proposed simplicity as a criterion.[1] One should choose the mathematically simplest or most elegant approach. Popper's solution was subsequently criticized by Thomas S. Kuhn in The Structure of Scientific Revolutions. Explanatory power. Explanatory power is the ability of a hypothesis to effectively explain the subject matter it pertains to. One theory is sometimes said to have more explanatory power than another theory about the same subject matter if it offers greater predictive power. That is, if it offers more details about what we should expect to see, and what we should not. Explanatory power may also suggest that more details of causal relations are provided, or that more facts are accounted for.
Scientist David Deutsch adds that a good theory is not just predictive and falsifiable (i.e. testable); a good explanation also provides specific details which fit together so tightly that it is difficult to change one detail without affecting the whole theory. The opposite of explanatory power is explanatory impotence. Overview[edit] Deutsch says that the truth consists of detailed and "hard to vary assertions about reality" Deutsch takes examples from Greek mythology. References[edit] Experiment. Even very young children perform rudimentary experiments in order to learn about the world. An experiment is an orderly procedure carried out with the goal of verifying, refuting, or establishing the validity of a hypothesis. Controlled experiments provide insight into cause-and-effect by demonstrating what outcome occurs when a particular factor is manipulated.
Controlled experiments vary greatly in their goal and scale, but always rely on repeatable procedure and logical analysis of the results. There also exist natural experimental studies. A child may carry out basic experiments to understand the nature of gravity, while teams of scientists may take years of systematic investigation to advance the understanding of a phenomenon. Overview[edit] In the scientific method, an experiment is an empirical method that arbitrates between competing models or hypotheses.[1][2] Experimentation is also used to test existing theories or new hypotheses in order to support them or disprove them.[3][4] Knowledge management experiment. Computer science experiment.
Cross-validation (statistics) Statistical model validation technique In a prediction problem, a model is usually given a dataset of known data on which training is run (training dataset), and a dataset of unknown data (or first seen data) against which the model is tested (called the validation dataset or testing set).[8][9] The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias[10] and to give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem). In summary, cross-validation combines (averages) measures of fitness in prediction to derive a more accurate estimate of model prediction performance.[11] Example: linear regression [edit] In linear regression, there exist real response values , and n p-dimensional vector covariates x1, ..., xn.
Exhaustive cross-validation Leave-p-out cross-validation is the binomial coefficient. . Reproducibility. Aristotle′s conception about the knowledge of the individual being considered unscientific is due to lack of the field of statistics in his time, so he could not appeal to statistical averaging of the individual. History[edit] Boyle's air pump was, in terms of the 17th Century, a complicated and expensive scientific apparatus, making reproducibility of results difficult The first to stress the importance of reproducibility in science was the Irish chemist Robert Boyle, in England in the 17th century.
Boyle's air pump was designed to generate and study vacuum, which at the time was a very controversial concept. The air pump, which in the 17th century was a complicated and expensive apparatus to build, also led to one of the first documented disputes over the reproducibility of a particular scientific phenomenon. Reproducible data[edit] Reproducibility is one component of the precision of a measurement or test method. Reproducible research[edit] Noteworthy irreproducible results[edit] Cognitive map. Overview[edit] Cognitive maps serve the construction and accumulation of spatial knowledge, allowing the "mind's eye" to visualize images in order to reduce cognitive load, enhance recall and learning of information.
This type of spatial thinking can also be used as a metaphor for non-spatial tasks, where people performing non-spatial tasks involving memory and imaging use spatial knowledge to aid in processing the task.[6] The neural correlates of a cognitive map have been speculated to be the place cell system in the hippocampus[7] and the recently discovered grid cells in the entorhinal cortex.[8] Neurological basis[edit] Cognitive mapping is believed to largely be a function of the hippocampus.
The hippocampus is connected to the rest of the brain in such a way that it is ideal for integrating both spatial and nonspatial information. Numerous studies by O'Keefe have implicated the involvement of place cells. Parallel map theory[edit] Generation[edit] History[edit] Criticism[edit] What is hypothesis and give me some examples?
Hypothetico-deductive model. The hypothetico-deductive model or method is a proposed description of scientific method . According to it, scientific inquiry proceeds by formulating a hypothesis in a form that could conceivably be falsified by a test on observable data. A test that could and does run contrary to predictions of the hypothesis is taken as a falsification of the hypothesis. A test that could but does not run contrary to the hypothesis corroborates the theory. It is then proposed to compare the explanatory value of competing hypotheses by testing how stringently they are corroborated by their predictions. Example [ edit ] One example of an algorithmic statement of the hypothetico-deductive method is as follows: [ 1 ] 1 . 2 . 3 . 4 .
One possible sequence in this model would be 1 , 2 , 3 , 4 . Note that this method can never absolutely verify (prove the truth of) 2 . Discussion [ edit ] Qualification of corroborating evidence is sometimes raised as philosophically problematic. Citations [ edit ] Complexity. There is no absolute definition of what complexity means, the only consensus among researchers is that there is no agreement about the specific definition of complexity. However, a characterization of what is complex is possible.[1] Complexity is generally used to characterize something with many parts where those parts interact with each other in multiple ways.
The study of these complex linkages is the main goal of complex systems theory. In science,[2] there are at this time a number of approaches to characterizing complexity, many of which are reflected in this article. Neil Johnson admits that "even among scientists, there is no unique definition of complexity - and the scientific notion has traditionally been conveyed using particular examples... " Ultimately he adopts the definition of 'complexity science' as "the study of the phenomena which emerge from a collection of interacting objects Overview[edit] Disorganized complexity vs. organized complexity[edit] Study of complexity[edit] Competition. Competition in sports. A selection of images showing some of the sporting events that are classed as athletics competitions. Consequences[edit] Competition can have both beneficial and detrimental effects.
Many evolutionary biologists view inter-species and intra-species competition as the driving force of adaptation, and ultimately of evolution. However, some biologists, most famously Richard Dawkins, prefer to think of evolution in terms of competition between single genes, which have the welfare of the organism 'in mind' only insofar as that welfare furthers their own selfish drives for replication. Biology and ecology[edit] Economics and business[edit] Experts have also questioned the constructiveness of competition in profitability. Three levels of economic competition have been classified: In addition, companies also compete for financing on the capital markets (equity or debt) in order to generate the necessary cash for their operations. Interstate[edit] Law[edit] Politics[edit]
Algorithm. Flow chart of an algorithm (Euclid's algorithm) for calculating the greatest common divisor (g.c.d.) of two numbers a and b in locations named A and B. The algorithm proceeds by successive subtractions in two loops: IF the test B ≥ A yields "yes" (or true) (more accurately the numberb in location B is greater than or equal to the numbera in location A) THEN, the algorithm specifies B ← B − A (meaning the number b − a replaces the old b). Similarly, IF A > B, THEN A ← A − B. The process terminates when (the contents of) B is 0, yielding the g.c.d. in A. (Algorithm derived from Scott 2009:13; symbols and drawing style from Tausworthe 1977). In mathematics and computer science, an algorithm ( i/ˈælɡərɪðəm/ AL-gə-ri-dhəm) is a step-by-step procedure for calculations. Algorithms are used for calculation, data processing, and automated reasoning.
Informal definition[edit] Boolos & Jeffrey (1974, 1999) offer an informal meaning of the word in the following quotation: Formalization[edit] ☢️ Cognitive.