1.
Experiment
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An experiment is a procedure carried out to support, refute, or validate a hypothesis. Experiments provide insight into cause-and-effect by demonstrating what outcome occurs when a particular factor is manipulated, experiments vary greatly in goal and scale, but always rely on repeatable procedure and logical analysis of the results. There also exists natural experimental studies, a child may carry out basic experiments to understand gravity, while teams of scientists may take years of systematic investigation to advance their understanding of a phenomenon. Experiments and other types of activities are very important to student learning in the science classroom. Experiments can raise test scores and help a student become more engaged and interested in the material they are learning, experiments can vary from personal and informal natural comparisons, to highly controlled. Uses of experiments vary considerably between the natural and human sciences, experiments typically include controls, which are designed to minimize the effects of variables other than the single independent variable. This increases the reliability of the results, often through a comparison between control measurements and the other measurements, scientific controls are a part of the scientific method. Ideally, all variables in an experiment are controlled and none are uncontrolled, in such an experiment, if all controls work as expected, it is possible to conclude that the experiment works as intended, and that results are due to the effect of the tested variable. In the scientific method, an experiment is a procedure that arbitrates between competing models or hypotheses. Researchers also use experimentation to test existing theories or new hypotheses to support or disprove them, an experiment usually tests a hypothesis, which is an expectation about how a particular process or phenomenon works. However, an experiment may also aim to answer a question, without a specific expectation about what the experiment reveals. If an experiment is conducted, the results usually either support or disprove the hypothesis. According to some philosophies of science, an experiment can never prove a hypothesis, on the other hand, an experiment that provides a counterexample can disprove a theory or hypothesis. An experiment must also control the possible confounding factors—any factors that would mar the accuracy or repeatability of the experiment or the ability to interpret the results, confounding is commonly eliminated through scientific controls and/or, in randomized experiments, through random assignment. In engineering and the sciences, experiments are a primary component of the scientific method. They are used to test theories and hypotheses about how physical processes work under particular conditions, typically, experiments in these fields focus on replication of identical procedures in hopes of producing identical results in each replication. In medicine and the sciences, the prevalence of experimental research varies widely across disciplines. In contrast to norms in the sciences, the focus is typically on the average treatment effect or another test statistic produced by the experiment
2.
Dependent and independent variables
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In mathematical modelling and statistical modelling, there are dependent and independent variables. The models investigate how the former depend on the latter, the dependent variables represent the output or outcome whose variation is being studied. The independent variables represent inputs or causes, i. e. potential reasons for variation, models test or explain the effects that the independent variables have on the dependent variables. Sometimes, independent variables may be included for other reasons, such as for their potential confounding effect, in mathematics, a function is a rule for taking an input and providing an output. A symbol that stands for an input is called an independent variable, while a symbol that stands for an arbitrary output is called a dependent variable. The most common symbol for the input is x, and the most common symbol for the output is y and it is possible to have multiple independent variables and/or multiple dependent variables. For instance, in calculus, one often encounters functions of the form z = f. Functions with multiple outputs are often written as vector-valued functions. In Set Theory, a function between a set X and a set Y is a subset of the Cartesian product X × Y such that every element of X appears in a pair with exactly one element of Y. However, many advanced textbooks do not distinguish between dependent and independent variables, in an experiment, the dependent variable is the event expected to change when the independent variable is manipulated. In data mining tools, the variable is assigned a role as target variable, while a dependent variable may be assigned a role as regular variable. Known values for the target variable are provided for the data set and test data set. The target variable is used in supervised learning algorithms but not in non-supervised learning, in mathematical modelling, the dependent variable is studied to see if and how much it varies as the independent variables vary. In the simple stochastic linear model y i = a + b x i + e i the term y i is the i th value of the dependent variable and x i is i th value of the independent variable. The term e i is known as the error and contains the variability of the dependent variable not explained by the independent variable, with multiple independent variables, the expression is, y i = a + b x 1 + b x 2 +. + b x n + e i, where n is the number of independent variables, in simulation, the dependent variable is changed in response to changes in the independent variables. If the independent variable is referred to as an explanatory variable then the response variable is preferred by some authors for the dependent variable. Explained variable is preferred by some authors over dependent variable when the quantities treated as dependent variables may not be statistically dependent, if the dependent variable is referred to as an explained variable then the term predictor variable is preferred by some authors for the independent variable
3.
Scientific method
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The scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. To be termed scientific, a method of inquiry is commonly based on empirical or measurable evidence subject to specific principles of reasoning, experiments need to be designed to test hypotheses. The most important part of the method is the experiment. The scientific method is a process, which usually begins with observations about the natural world. Human beings are naturally inquisitive, so often come up with questions about things they see or hear. The best hypotheses lead to predictions that can be tested in various ways, in general, the strongest tests of hypotheses come from carefully controlled and replicated experiments that gather empirical data. Depending on how well the tests match the predictions, the hypothesis may require refinement. If a particular hypothesis becomes very well supported a theory may be developed. Although procedures vary from one field of inquiry to another, identifiable features are shared in common between them. The overall process of the method involves making conjectures, deriving predictions from them as logical consequences. A hypothesis is a conjecture, based on knowledge obtained while formulating the question, the hypothesis might be very specific or it might be broad. Scientists then test hypotheses by conducting experiments, the purpose of an experiment is to determine whether observations agree with or conflict with the predictions derived from a hypothesis. Experiments can take anywhere from a college lab to CERNs Large Hadron Collider. There are difficulties in a statement of method, however. Though the scientific method is presented as a fixed sequence of steps. Not all steps take place in scientific inquiry, and are not always in the same order. Some philosophers and scientists have argued there is no scientific method, such as Lee Smolin. Nola and Sankey remark that For some, the idea of a theory of scientific method is yester-years debate
4.
Design of experiments
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The design of experiments is the design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation. In its simplest form, an experiment aims at predicting the outcome by introducing a change of the preconditions, the change in the predictor is generally hypothesized to result in a change in the second variable, hence called the outcome variable. Main concerns in design include the establishment of validity, reliability. Related concerns include achieving appropriate levels of power and sensitivity. Correctly designed experiments advance knowledge in the natural and social sciences, other applications include marketing and policy making. In 1747, while serving as surgeon on HMS Salisbury, James Lind carried out a clinical trial to compare remedies for scurvy. This systematic clinical trial constitutes a type of DOE, Lind selected 12 men from the ship, all suffering from scurvy. Lind limited his subjects to men who were as similar as I could have them and he divided them into six pairs, giving each pair different supplements to their basic diet for two weeks. The treatments were all remedies that had proposed, A quart of cider every day. Twenty five gutts of vitriol three times a day upon an empty stomach, one half-pint of seawater every day. A mixture of garlic, mustard, and horseradish in a lump the size of a nutmeg, two spoonfuls of vinegar three times a day. Two oranges and one every day. The citrus treatment stopped after six days when they ran out of fruit, apart from that, only group one showed some effect of its treatment. The remainder of the crew served as a control. Charles S. Peirce randomly assigned volunteers to a blinded, repeated-measures design to evaluate their ability to discriminate weights, peirces experiment inspired other researchers in psychology and education, which developed a research tradition of randomized experiments in laboratories and specialized textbooks in the 1800s. Charles S. Peirce also contributed the first English-language publication on a design for regression models in 1876. A pioneering optimal design for regression was suggested by Gergonne in 1815. In 1918 Kirstine Smith published optimal designs for polynomials of degree six, herman Chernoff wrote an overview of optimal sequential designs, while adaptive designs have been surveyed by S. Zacks
5.
Polyacrylamide gel electrophoresis
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Mobility is a function of the length, conformation and charge of the molecule. For nucleic acids, urea is the most commonly used denaturant, for proteins, sodium dodecyl sulfate is an anionic detergent applied to protein samples to linearize proteins and to impart a negative charge to linearized proteins. In most proteins, the binding of SDS to the polypeptide chain imparts an even distribution of charge per unit mass, samples may be any material containing proteins or nucleic acids. These may be derived, for example from prokaryotic or eukaryotic cells, tissues, viruses, environmental samples. Synthetic biomolecules such as oligonucleotides may also be used as analytes, the sample to analyze is optionally mixed with a chemical denaturant if so desired, usually SDS for proteins or urea for nucleic acids. SDS is a detergent that denatures secondary and non–disulfide–linked tertiary structures. Urea breaks the bonds between the base pairs of the nucleic acid, causing the constituent strands to separate. Heating the samples to at least 60 °C further promotes denaturation and this is known as reducing SDS-PAGE. A tracking dye may be added to the solution and this typically has a higher electrophoretic mobility than the analytes to allow the experimenter to track the progress of the solution through the gel during the electrophoretic run. The gels typically consist of acrylamide, bisacrylamide, the optional denaturant, the solution may be degassed under a vacuum to prevent the formation of air bubbles during polymerization. Alternatively, butanol may be added to the gel after it is poured. A source of radicals and a stabilizer, such as ammonium persulfate. The polymerization reaction creates a gel because of the added bisacrylamide, the ratio of bisacrylamide to acrylamide can be varied for special purposes, but is generally about 1 part in 35. The acrylamide concentration of the gel can also be varied, generally in the range from 5% to 25%, lower percentage gels are better for resolving very high molecular weight molecules, while much higher percentages are needed to resolve smaller proteins. Gels are usually polymerized between two plates in a gel caster, with a comb inserted at the top to create the sample wells. After the gel is polymerized the comb can be removed and the gel is ready for electrophoresis, various buffer systems are used in PAGE depending on the nature of the sample and the experimental objective. The buffers used at the anode and cathode may be the same or different, an electric field is applied across the gel, causing the negatively charged proteins or nucleic acids to migrate across the gel away from the negative electrode and towards the positive electrode. Depending on their size, each biomolecule moves differently through the gel matrix, small molecules more easily fit through the pores in the gel, after the set amount of time, the biomolecules have migrated different distances based on their size
6.
Confounding
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In statistics, a confounding variable is a variable in a quantitative research study that explains some or all of the correlation between the dependent variable and an independent variable. A confounder is a variable that is correlated to the independent variable, a lurking variable is a covariate that is not taken in account for by the researchers of the quantitative study. Confounding is a concept, and as such, cannot be described in terms of correlations or associations. The concept of confounding must be defined, and managed, in terms of the data generating model, specifically, let X be some independent variable, Y some dependent variable. To estimate the effect of X on Y, the statistician must suppress the effects of variables that influence both X and Y. We say that, X and Y are confounded by other variable Z whenever Z is a cause of both X and Y. In the causal framework, denote P as the probability of event Y = y under the hypothetical intervention X = x. X and Y are not confounded if and only if the following holds, for all values X = x and Y = y, where P is the conditional probability upon seeing X = x. In principle, the defining equality P = P can be verified from the data generating model assuming we have all the equations and this is done by simulating an intervention do and checking whether the resulting probability of Y equals the conditional probability P. It turns out, however, that graph structure alone is sufficient for verifying the equality P= P, consider a researcher attempting to assess the effectiveness of drug X, from population data in which drug usage was a patients choice. Data show that gender differences influence a patients choice of drug as well as their chances of recovery, in the case of a single confounder Z, this leads to the adjustment formula, which gives an unbiased estimate for the causal effect of X on Y. The same adjustment formula works when there are multiple confounders except, in this case, the criterion for a proper choice of variables is called the Back-Door and requires that the chosen set Z blocks every path from X to Y that ends with an arrow into X. Such sets are called Back-Door admissible and may include variables which are not common causes of X and Y, contrary to common beliefs, adding covariates to the adjustment set Z can introduce bias. A typical counterexample occurs when Z is an effect of X and Y. In general, confounding can be controlled by adjustment if and only if there is a set of observed covariates that satisfies the Back-Door condition, moreover, if Z is such a set, then the adjustment formula of Eq. is valid <4, 5>. Pearls do-calculus provide additional conditions under which P can be estimated, fisher used the word confounding in his 1935 book The Design of Experiments to denote any source of error in his ideal of randomized experiment. According to Vandenbroucke it was Kish who used the word confounding in the sense of the word. These were later supplemented by graphical criteria such as the Back-Door condition, graphical criteria were shown to be formally equivalent to the counterfactual definition, but more transparent to researchers relying on process models
7.
Sugar substitute
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A sugar substitute is a food additive that provides a sweet taste like that of sugar while containing significantly less food energy. Some sugar substitutes are produced by nature, and others produced synthetically and those that are not produced by nature are, in general, called artificial sweeteners. An important class of sugar substitutes is known as high-intensity sweeteners and these are compounds with many times the sweetness of sucrose, common table sugar. As a result, much less sweetener is required and energy contribution is often negligible, the sensation of sweetness caused by these compounds is sometimes notably different from sucrose, so they are often used in complex mixtures that achieve the most natural sweet sensation. If the sucrose that is replaced has contributed to the texture of the product, in the United States, seven intensely sweet sugar substitutes have been approved for use. They are stevia, aspartame, sucralose, neotame, acesulfame potassium, saccharin, cyclamates are used outside the U. S. but have been prohibited in the U. S. since 1969. Others, which may or may not be approved depending on jurisdiction, include allulose, the U. S. Food and Drug Administration regulates artificial sweeteners as food additives. Food additives must be approved by the FDA, which publishes a generally recognized as safe list of additives, stevia is exempt under the FDAs GRAS policy due to its being a natural substance in wide use well before 1958, and the FDA has approved it on these grounds. The conclusions about safety are based on a review of a large body of information. The majority of sugar substitutes approved for use are artificially synthesized compounds. However, some bulk natural sugar substitutes are known, including sorbitol and xylitol, which are found in berries, fruit, vegetables and it is not commercially viable to extract these products from fruits and vegetables, so they are produced by catalytic hydrogenation of the appropriate reducing sugar. For example, xylose is converted to xylitol, lactose to lactitol, other natural substitutes are known, but these have yet to gain official approval for food use. Sorbitol and xylitol are examples of sugar alcohols and these are, in general, less sweet than sucrose but have similar bulk properties and can be used in a wide range of food products. Sometimes the sweetness profile is fine-tuned by mixing these with high-intensity sweeteners, as with all food products, the development of a formulation to replace sucrose is a complex proprietary process. This allows them to eat the foods they normally would while allowing them to lose weight. Dental care – Carbohydrates and sugars usually adhere to the tooth enamel, the bacteria convert the sugar to acids that decay the teeth. Sugar substitutes, unlike sugar, do not erode teeth as they are not fermented by the microflora of the dental plaque. A sweetener that can actually benefit dental health is xylitol, which tends to prevent bacteria from adhering to the surface, thus preventing plaque formation
"Control". Encyclopædia Britannica. 7 (11th ed.). 1911.
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