The problem of distinguishing between science and pseudoscience is very complex. Currently, there are many pseudoscientific concepts, some of which try to present themselves as scientific. It is especially difficult to distinguish from scientific theories those that are created by scientists themselves and are either delusions or deliberate falsifications. Some rule is required that would allow us to distinguish a scientific concept from a pseudoscientific one already at the moment of its appearance. However, all attempts to find an exact formal criterion are still unsuccessful. There is no rule that would allow one to reliably determine the scientific nature of hypotheses.

Postpositivist philosophers K. Popper and T. Kuhn showed that scientific ideas change over time. Those theories that were once accepted as scientific may later be rejected as unscientific. Conversely, a too bold hypothesis, which was not initially recognized by the scientific community, could be classified as scientific after it was confirmed experimentally. The set of theories that are considered scientific has been different at different times. Therefore, it seems to us that it is hardly possible in principle to construct an exact criterion for such a changing object.

Wittgenstein proposed using family resemblances to characterize concepts with fuzzy boundaries. In Philosophical Investigations, Wittgenstein writes about language games and notes that there is no property that is common to all games. “We see a complex network of similarities, overlapping and intertwining with each other, similarities in large and small.” How should a criterion be constructed for a concept with fuzzy boundaries?

Let us first consider how the criterion is formulated if we consider the concept to be precisely defined. (An example of such concepts would be mathematical concepts.) The standard criterion is formulated as follows:

"An object x has property A if and only if x is in relation B1 with objects x1, x2, ..., xn; in relation B2 with objects y1, y2, ..., ym, etc."

Formally, this criterion can be written:

A(x) Û B1(x; x1, x2,.. xn) Ù B1(x; y1, y2,.. ym) Ù B1(x; z1, z2,.. zl).

where x is the name of the object being defined;

xi, yi, ..., zi – names of some objects;

A – one-place predicate;

B1, B2, …, Bk are some predicates that show the relationship of object x with objects.

If the concept has no clear boundaries, then we cannot necessarily require x to have the enumerated relations. Then, in the formulation of the criterion for fuzzy concepts, the conjunction of relations will be replaced by disjunction:

A(x) Û B1(x; x1, x2,.. xn) Ú B2(x; y1, y2,.. ym) Ú…Ú Bk (x; z1, z2,.. zl). (1) For x to have property A, it is necessary and sufficient that at least one condition be satisfied, that is, that at least one predicate B1, B2, ..., Bk be true.

However, this condition is not strict enough for our purposes. The fact is that some properties may be inherent in some pseudoscientific theory. We assume that a scientific hypothesis is characterized by a larger number of listed properties than a non-scientific one; therefore, in order to construct a working criterion, it is necessary to limit from below the number of characteristics that must be true

Let us denote by m minimum quantity properties or relations that an object x must have in order for us to say "x has property A". Considering that P(x) = 1 if P(x) is true and P(x) = 0 if P(x) is false, we formally write down a restriction on the number of relationships in which object x must be with objects xi, yi, ..., zi.

B1 (x; x1, x 2,.. xn) + B2 (x; y1, y2,.. ym) +…+ Bk (x; z1, z2,.. zl) ³ m. (2) where 1 £ m £ k.

Thus, condition (2) allows us to discard those objects that have an insufficient number of required characteristics. Now "x has property A" if and only if x has at least m properties and relations.

In reality, it often turns out that properties are not equivalent to each other. The presence of some properties may be more significant than the presence of some others. To explain this, let's look at an example.

Among the requirements that apply to scientific theories, there are, in particular, the requirements of logical consistency and empirical confirmability. If the theory being tested is natural science, then the requirement of empirical confirmability is more important. The requirement of logical consistency in the natural sciences is not so important. A new empirical theory, as a rule, at some point in time contradicts some of the established beliefs. However, if we are talking about mathematical theory, then the requirement of logical consistency is necessary.

Thus, we need to assign weights to our predicates, which we denote by bi. These weights make it possible to reflect the degree of significance of a particular attribute for objects of a given type.

b1 * B1(x; x1, x2,.. xn) + b2* B2(x; y1, y2,.. ym) +…+ bk* Bk (x; z1, z2,.. zl) ³ m. (2")

where bi are such that 0 £ bi< 1; и b1 + b2 +…+ bk = 1.

Thus, the final form of the criterion for fuzzy concepts, constructed according to the family resemblance rule, is formally written by formulas (1) and (2").

To demonstrate how a criterion constructed using the family resemblance rule for fuzzy concepts can be used, let us consider its application to assessing the scientific nature of a proposed hypothesis. Evaluating new theories for scientific validity is especially difficult at the time of their appearance. Therefore, in order to demonstrate how this criterion can be used, we will consider how this criterion is constructed to determine the scientific nature of a hypothesis.

The variable x denotes the hypothesis being tested for scientific validity, the one-place predicate A(x) has the value “true” if the hypothesis x is scientific. Based on the research of L.B. Bazhenov, we list the signs that characterize a scientific hypothesis. “A hypothesis differs from a simple guess by a number of very important limitations.” These restrictions are the following requirements:

· consistency with known facts;

· consistency of the new hypothesis with established theories;

· empirical testability;

· applicability to the widest possible range of phenomena;

· predictive power of the hypothesis;

· simplicity.

Let's look at these requirements in more detail.

The requirement of consistency with known facts means that a scientific hypothesis must be in agreement with known factual material. If we denote a proposition about facts by Ai, then this condition will be written as follows:

x Ù (A1 Ù A2 Ù… Ù An) a B ÙØ B,

where B is some affirmative sentence. However, this requirement cannot be necessary, since there are cases when the interpretation of facts must be revised under the influence of a hypothesis, and as a result the facts receive a new interpretation.

For example, when developing the wave hypothesis of light, Fresnel's hypothesis contradicted a seemingly obvious fact. If an opaque disk is placed between the screen and a point light source, a shadow in the shape of a circle is cast on the screen. From Fresnel's wave hypothesis it followed that there should be a small bright spot in the center of the shadow. More careful experiments showed that a light spot actually forms in the center of the shadow, so it was not a new hypothesis that was rejected, but a seemingly reliable fact.

For a hypothesis to be put forward, a necessary requirement is agreement with established laws. A scientific hypothesis is part of a system of developing scientific knowledge, therefore, it must be consistent with the basic established laws, theories, etc. If the set of established ideas is denoted as a set of statements T, then the requirement for the consistency of a new hypothesis x with the established ideas can be written in the form:

x È T a B Ù Ø B,

where B is some statement.

This requirement is not necessary, since newly put forward hypotheses often come into conflict with previously existing scientific positions, which ensures the progress of science.

The requirement of empirical testability of consequences is very important for determining the status of a hypothesis. A hypothesis contains assumptions about the causes of phenomena (explanatory hypothesis) and the connections between phenomena (descriptive hypothesis), which cannot be established directly from experience. A hypothesis is tested by comparing the consequences derived from the hypothesis with the facts. The ability to derive testable consequences allows us to move from assumptions to observable phenomena. A hypothesis may turn out to be empirically untestable, but allows for the possibility of indirect verification.

However, one should distinguish between the impossibility of testing a hypothesis, which is due to imperfect experimental techniques, and fundamental unobservability, when the observed consequences cannot be deduced in principle. Those hypotheses that are fundamentally unobservable should be denied scientific validity. This requirement protects science from introducing into it non-manifesting entities, some kind of “things in themselves.” The requirement for the deducibility of observed consequences can be written in the form [(x È T) a A] Ù, where A is a sentence of observation. The requirement that a hypothesis be applicable to the widest possible range of phenomena limits access to science to the access of ad hoc hypotheses. A hypothesis, originally put forward to explain a certain phenomenon, should be able, with some adjustments, to describe a wider class of phenomena. If a hypothesis is invented to explain only some experimental fact and does not lead to any other consequences, then it has the character of an ad hoc hypothesis. A truly scientific hypothesis goes beyond a narrow field of phenomena and allows one to predict new phenomena, relationships and laws. This requirement also cannot be absolute, since hypotheses can also be put forward about unique phenomena. (For example, about the movements of comets.)

The predictive power of a hypothesis makes it fruitful for the discovery of new phenomena, facts and relationships.

The requirement of simplicity of the hypothesis requires that as many phenomena as possible be explained through as few causes as possible. This requirement reflects the belief of scientists in the existence of some single objective structure of the world. For the sake of simplicity, only hypotheses put forward to explain similar phenomena can be compared with each other.

This list of properties may not be perfect. It may need to be supplemented with new requirements, or it may be that some of the above properties are redundant. This shortcoming of the above criterion for the scientific nature of a hypothesis, based on the rule of family resemblances, is easily corrected by changing the composition of the predicates.

It is possible that none of the scientific hypotheses being tested will have all of the listed qualities at the same time. It is also possible that there are pseudoscientific theories that may have some of these properties. Therefore, you will need to set some acceptable minimum m number of properties. To determine this number, it is necessary to carry out a calibration - to consider a number of examples of scientific and non-scientific hypotheses and calculate the number of properties that were inherent in both. It should be taken into account that over time the composition and importance of the requirements that were imposed on scientific theories could change. The determination of the value of this number is a matter of convention and depends, in particular, on the total number of characteristics.

The closer this number is to total number characteristics, the more stringent the criterion. The setting of the values ​​of the weights bi is also a matter of convention and depends in particular on the specific application. For example, if the criterion is used to evaluate historical hypotheses, then the requirement that the hypothesis is applicable to the widest possible range of phenomena is insignificant, since historical science deals with individual phenomena, therefore vanishingly small weight can be assigned to the corresponding coefficient bi.

Among the advantages of the criterion based on the rule of family resemblances, the following can be noted. It better reflects the state of affairs in the case of unclear concepts. The ability to change and rebuild the criterion in case of changes in the composition of requirements and their significance in at the moment time and for a given application.

This criterion shifts the problem from the realm of vague philosophical reasoning to the realm of tests that are accessible intersubjectively. (Logical analysis, empirical testability.)

Working with the criterion assumes the active role of the scientific community in resolving issues of the composition of properties, determining the degree of their significance, and the number of properties that must be fulfilled. In addition, this criterion allows for quantitative assessment.

Among the shortcomings of the criterion are the following. In the construction of the criterion, the convention plays too large a role, which does not exclude the possibility of speculation. Therefore, testing the criterion on a certain number of examples is required. However, during such a test, attention should be paid to the fact that the requirements for scientific theories may be different at different times, and it is advisable to test the criterion on examples of those hypotheses that have requirements similar to modern ones.

The decisive role is assigned to the scientific team, which is a complex subject and, therefore, is not immune from errors arising from subjective vision.

Scientific hypotheses, in the normal course of scientific development, undergo natural selection. There is an opinion that if non-specialists do not interfere in the development of science, then the danger of the emergence of pseudoscientific theories simply does not arise. “If the scientific value of the work is determined not by an order from an administrator, but by the public opinion of large groups, the likelihood of error is minimal.” However, administrative structures are guided, as a rule, not by the scientific value of the theory they support or reject, but by political interests. If this is so, then the proposed criterion is useless.

This criterion cannot provide insight into the mechanisms for selecting alternative theories. Our preferences that determine our choices are often irrational. However, it is possible that a criterion constructed using the family resemblance rule will distinguish between false and unscientific theories.

Hypotheses- reasonable assumptions about the structure of the objects under study, the nature of the connections between the phenomena being studied and possible approaches to solving problems

Requirements for hypotheses:

1. The hypothesis should not contain concepts that have not received empirical interpretation, otherwise it is not testable.

2. It should not contradict previously established scientific facts.

3. It should be simple (fewer possible assumptions).

4. A good hypothesis applies to a wider range of phenomena.

5. The hypothesis must be fundamentally testable at a given level of knowledge.

6. The formulation of the hypothesis should not contain unclear terms; the expected connection of events should be clearly indicated.

Hypothesis is an assumption, the researcher’s answer to the main question scientific research. Since a hypothesis is just an assumption, it needs to be tested (proof or refutation).

There are: theoretical, empirical, basic, additional, alternative.

Theoretical hypotheses are put forward to eliminate internal contradictions in theory or to overcome discrepancies between theory and experimental results and are a tool for improving theoretical knowledge. Hypotheses - empirical assumptions are put forward to solve a problem using the method of experimental research. Therefore, they are also called experimental hypotheses.

There are three levels of experimental hypotheses according to their origin.

1. Theoretically based hypotheses - are based on theories or models of reality and represent predictions, consequences of these theories or models. Hypotheses at this level serve to test the implications of a particular theory or model.

2. Scientific experimental hypotheses - put forward to confirm or refute certain theories, laws, previously discovered patterns or causal relationships between phenomena. They differ from first-level hypotheses in that they are not based on existing theories.

3. Empirical hypotheses - put forward without regard to any theory or model, i.e. they are formulated for a given case. After experimental testing, such a hypothesis turns into a fact.

The hallmarks of a productive hypothesis are adequacy, veracity and testability. The adequacy of the hypothesis lies in the correspondence of the research theory to its goals and objectives, as well as in its correlation with the reality being studied. The truthfulness of the hypothesis lies in the fact that it is based on real and scientifically proven facts and contains the logic of common sense. The possibility of testing a hypothesis appears in two principles: falsifiability and verifiability. The principle of falsifiability is that a hypothesis can be disproved during an experiment. This principle is absolute, since the refutation of a theory is always final. The principle of verifiability is that during the experiment the hypothesis is confirmed. This principle is relative, since there is always the possibility of a hypothesis being rejected in the next study.

Topic 2.4 Interpretation and presentation of results psychological research. /2 lec./

Questions:

1. Processing of psychological research data.

Requirements that may exist. presented for acceptance at

study of SU hypotheses may be as follows:

• - purposefulness, providing an explanation of all the facts characterizing the problem being solved;
• - relevance (English) relevant - relevant, appropriate), i.e. based on facts and ensuring the admissibility of its recognition both in science and in practice. If a hypothesis does not use facts, then it is called irrelevant;
• - predictiveness, providing forecasting of research results;
• - testability, which allows the fundamental possibility of testing a hypothesis empirically based on observations or experiments. This should ensure its refutation (falsifiability) or confirmation (verifiability). However, it cannot be said that all hypotheses can be tested. These include: firstly, those that cannot be verified at the present time due to imperfection technical means, laws and regularities that have not yet been discovered, etc.; secondly, hypotheses that are fundamentally untestable based on facts; thirdly, universal mathematical hypotheses related to abstract objects of research and not allowing empirical confirmation;
• - consistency achieved by the logical consistency of all structural components hypotheses;
• - compatibility, ensuring the connection of the proposed assumptions with existing scientific theoretical and practical knowledge. In case of incompatibility and contradictions between the put forward hypothesis and existing knowledge, it is necessary to check the laws and facts on which the hypothesis in question and previous knowledge are based;
• - potentiality, including the possibility of using a hypothesis based on the quantity and quality of deductive conclusions and consequences arising from it, their strength and influence on the development of system management;
• - simplicity, based on consistency and a smaller number of initial premises contained in the hypothesis to obtain conclusions and consequences; and also enough large number the facts it explains. In this case, the hypothesis can at the same time be of a more general nature. The simplicity of the hypothesis, of course, cannot exclude the use of complex mathematical apparatus to confirm it.

Fulfillment of the above requirements distinguishes an accepted scientific hypothesis from an ordinary guess. In this case, relatively many questions arise related to the confirmation or refutation of hypotheses. However, the most important criterion for one or the other, i.e. The truth of a hypothesis is still its empirical verifiability. This is where the difficulty of testing them comes into play.

It is obvious that between confirmation and refutation of a hypothesis there is essentially a complete opposite. However, if the meaning of confirmation is, as a rule, relatively temporary, then refutation is final. Moreover, to refute it, a deductive substantiation of the falsity of just one consequence of a hypothesis is sufficient, and it is unlawful to confirm its truth on the basis of proof of part of the statements. In the latter case, the conclusion is made using the inductive method. In addition, when considering interrelated statements and the validity of each of them separately, it is impossible to draw a conclusion about the truth of the entire hypothesis or several interconnected hypotheses in a larger number of cases, since synergistic properties may appear when the statements in the hypothesis interact. Therefore, when confirming, including testing, the truth of hypotheses, it is advisable to use a systematic approach.

Hypothesis development

The formation of hypotheses is one of the difficult and poorly formalized research processes. However, the entire process of forming and developing hypotheses in the context of the entire study can be divided into a number of stages, which for most cases should include, in particular:

• - preparatory stage: collecting information and identifying the problem; definition of a specific object and subject of research; setting goals and objectives of the study; accumulation and preliminary analysis of factual material, formulation of primary assumptions (working hypotheses) on its basis;
• - formative: analysis of available information and determination of the causes of the problem, its content and characteristics; identification of factors influencing the problem and their connections; identifying the consequences of the formulated assumptions and determining the expected results on their basis; collection of facts and data necessary to evaluate the accuracy made on the basis of hypothetical assumptions; determination of conditions, ways and methods for solving problems; formulation of initial hypotheses.

Subsequently, all the stages and work that are provided for by the research methodology are carried out, including: planning, organizing and conducting experiments, analyzing and summarizing the results obtained; verification of the correctness and reliability of the obtained expected results in practice and clarification of hypotheses based on the results of such verification. If hypotheses do not correspond to actual results, they must be reviewed and adjusted as necessary.

When forming hypotheses, it is very important to correctly use the possible methods for this. It should be noted that logical methods are less suitable for searching for scientific truth in experimental sciences (for example, in physics, etc.), but for socio-economic systems they cannot be underestimated. They are especially effective in combination with deductive-inductive rules for developing hypotheses, as well as in conjunction with information abstraction. Abstraction makes it possible to eliminate unnecessary irrelevant information that may make it difficult to make simple and realistic assumptions and, ultimately, formulate a valid hypothesis.

The results of using various methods in forming hypotheses largely depend not only on the availability of available information, but also on the level of general knowledge, the depth of penetration of the researcher into the problem being studied, experience and intuition. If a hypothesis does not withstand a number of tests, then it is refuted or completely rejected.

If confirmed, such a hypothesis in some cases can acquire the status of a theory. It should be noted that in general, theory (Greek - observation, consideration, research) can be understood as a doctrine, as a set of generalizing fundamental scientific concepts, ideas and methodological provisions, existing experience and practice, forming one or another branch (sub-branch) of knowledge, objectively reflecting the laws and patterns of its development. At the same time, theory is also considered as a developed form of systematization and organization of scientific knowledge, allowing a holistic perception of certain phenomena of reality. Obviously, the most important basic components of a theory are the initial concepts, ideas, laws, patterns and idealized or abstract objects. The theory, having its own logic, allows you to substantiate new statements based on previously existing ones.

Hypothesis requirements

The following requirements apply to the hypothesis:

It should not include too many provisions: as a rule, one main one, rarely more;

It cannot include concepts and categories that are not unambiguous and not understood by the researcher himself;

When formulating a hypothesis, value judgments should be avoided; the hypothesis must correspond to the facts, be testable and applicable to a wide range of phenomena;

An impeccable stylistic design, logical simplicity, and respect for continuity are required.

The hypothesis must correspond to the topic, the assigned tasks and not go beyond the scope of the research subject. Often there are interesting hypotheses that turn out to be only artificially tied to the problem.

A hypothesis should aim at solving the problem, and not lead away from it. You can’t let your imagination lead you into the jungle of problems. It is better to deepen and expand the hypothesis as new facts accumulate, than to initially build too many assumptions, which sometimes require many years of work by an entire scientific team to test, or which do not even make sense to test due to their abstractness, isolation from science and practice, and scholasticity.

A hypothesis must correspond to well-tested facts, explain them, and predict new ones. Of the hypotheses that must explain a whole series of facts, preference is given to the one that uniformly explains the largest number of facts.

A hypothesis that explains phenomena in a certain area must not contradict other theories in the same area that have already been proven to be true. If a new hypothesis conflicts with already known ones, but at the same time covers a wider range of phenomena than in previous theories, then the latter become a special case of a new, more general theory.

The hypothesis must be testable. Assumptions remain as such unless they can be tested and proven; with rare exceptions, they cannot be included in the fund of science as a theoretical value, as scientific foundation knowledge. The researcher’s action will be fair if, following scientific conclusions, he reveals hypothetical provisions of his scientific research that could not be verified.

A scientific hypothesis must contain a project for solving a problem in theory and in practice. Then it will become an organic part of the research.

To realize these requirements, when developing a hypothesis, it is recommended to consistently think through and answer the following questions:

1. What is most significant in the subject of research (the process of quality formation, the connection between pedagogical phenomena, the characteristics of a pedagogical phenomenon, process, the formation of relations between subjects of educational, sports activities, etc.)?

2. What are the constituent elements of the object of study that make up the quality being studied, types of relationships, groups of properties, signs of pedagogical phenomena, etc., since their structure is necessary for the hypothesis.

3. What are the model of the process being studied, personality traits, qualities? How can you diagrammatically represent the constituent elements and connections between them? What data is there for such a model? What assumptions can be made based on indirect data and intuition?

4. How is the process or phenomenon supposed to proceed, what happens to the elements during the development of the phenomenon? How does their connection change due to changes in external conditions and pedagogical influences? What is the dialectic of the connection between external conditions and internal factors during the normal, accelerated and incorrect course of a process or phenomenon?

5. What is the essence of the process or phenomenon being studied? These are the main provisions that determine improving the quality of constructing and using a hypothesis as a methodological basis for pedagogical research.

The main stages of constructing hypotheses

The main stages of constructing hypotheses can be divided into three parts:

Proposing hypotheses is the main type of scientific creativity associated with the objective need for new knowledge. In this case, the hypothesis put forward should be:

theoretically reliable, consistent with previous knowledge, not contradicting the facts of science;

Logically consistent with the problem and goal;

Include concepts that have received preliminary clarification and interpretation;

Applicable to data contained in a preliminary description of the subject of research;

Provide the opportunity for empirical testing (verification) with the help of subject-specific and methodological means of cognition, which ensures the transition from it to theory and law.

2. Formulation (development) of hypotheses. The hypothesis put forward must be formulated. The course and result of its testing depend on the correctness, clarity and certainty of the formulation of the hypothesis.

3. Testing hypotheses. Proof and reliability of hypotheses becomes the main task of subsequent empirical research. confirmed hypotheses become theory and law and are used for implementation in practice. Those that are not confirmed are either discarded or become the basis for putting forward new hypotheses and new directions in the study of the problem situation.

5.Functions of hypotheses in scientific research.

Hypotheses are present at all stages of scientific research, regardless of its nature - fundamental or applied, but their application is most pronounced in the following cases:

1) generalization and summation of the results of observations and experiments,

2) interpretation of the obtained generalizations,

3) justification of some previously introduced assumptions and

4) planning experiments to obtain new data or test certain assumptions.

Hypotheses are so common in science that scientists sometimes do not even notice the hypothetical nature of knowledge and believe that research is possible without premises in the form of hypotheses. However, this opinion is clearly wrong. As mentioned above, research consists of setting, formulating and solving a problem, and each problem arises only within some preliminary knowledge containing hypotheses and even the premise of the problem is hypothetical.

Let's consider the main functions of hypotheses in science.

First, hypotheses are used to generalize experience, summarize and presumably expand existing empirical data. The most famous type of such hypotheses that generalize existing experience is the transfer of the properties of a number of elements of a certain class to the entire class under consideration using the methods of classical enumerative induction. Another example of hypotheses of this class can be the so-called “empirical curves”, connecting series of observational data represented by points on the coordinate plane. In fact, even the representation of quantitative data on a coordinate plane by points is to a certain extent hypothetical, since measurement errors are always permissible or their accuracy is limited to a very certain limit.

Secondly, hypotheses can be premises of a deductive conclusion, i.e. arbitrary assumptions of a hypothetico-deductive scheme, working hypotheses or simplifying assumptions accepted even when their truth is doubtful.

Thirdly, hypotheses are used to orient the research and give it direction. This function is performed by partially (empirically or theoretically) substantiated hypotheses, which are also the object of research. Performing this function, the hypothesis appears either in the form of a working one, or in the form of preliminary and inaccurate statements of a programmatic nature, for example, “Living organisms can be synthesized by reproducing the physical conditions of our planet that took place 2 billion years ago,” etc.

Fourth, hypotheses are used to interpret empirical data or other hypotheses. All representational hypotheses are interpretive because they allow us to explain previously obtained phenomenological hypotheses.

Fifth, hypotheses can be used to defend other hypotheses in the face of new experimental data or identified contradictions with previously existing knowledge. Thus, W. Harvey (1628) introduced the assumption of blood circulation, which contradicted experimental data on the difference in composition of venous and arterial blood; To protect the original assumption from this experimental refutation, he introduced a defensive hypothesis about the closure of the arterial circulation by invisible capillaries, which were later opened.

In conclusion of the above, we can conclude that hypotheses are an irreducible element of empirical sciences, a special form of development of natural science, i.e. a hypothesis is a form of development of biological knowledge.

Scientific research as such consists of the study of problems involving the formulation, development and testing of hypotheses. The bolder a hypothesis is, the more it explains and the greater the degree of testability. However, in order to be scientific, an assumption must be justified and testable, which excludes from the field of science adhoc hypotheses and hypotheses introduced only on the basis of their formal elegance and simplicity. The task in scientific research is not an attempt to avoid the use of hypotheses altogether, but to introduce them consciously, since the development of knowledge is in principle impossible without assumptions that go beyond the scope of a given experience, in particular in the development of biological knowledge [8, p. 76-97].

Conclusion

In conclusion, we will draw some conclusions based on everything said above and given as an example.

The direct definition of a hypothesis sounds something like this: A hypothesis is a scientifically based assumption that serves to explain a fact, a phenomenon that is inexplicable on the basis of previous knowledge.

A hypothesis is not yet true; it does not possess the property of truth in the mind of the researcher who put it forward.

A hypothesis is supposedly new knowledge (its truth or falsity must be proven), obtained by extrapolating old knowledge and at the same time breaking with it. While maintaining a certain continuity with respect to past knowledge, the hypothesis must contain fundamentally new knowledge.

The very fact that a hypothesis is a form of development, the movement of any knowledge, reveals its dialectical nature: it is a necessary form of transition from the unknown to the known, the stage of transformation of the first into the second, probable knowledge into reliable, relative into absolute. If there are no hypotheses in science, then this means that there are no problems in it to solve which they are aimed at, and therefore knowledge does not develop in it.

So, we see that scientific research includes two points:

1) statement of the problem and

2) formulation of the hypothesis.

If the outcome is favorable, when the hypothesis is confirmed, the search ends with a discovery. Discovery forms the third and final stage of the search.

List of used literature

1.M.Ya.Vilensky/electronic resource/ http://lib.sportedu.ru/press/tpfk/1997N5/p15-17.htm

An important role is played by a hypothesis (from the Greek hypothesis - assumption) - a scientific preliminary insufficiently proven explanation (assumption, prediction) of new phenomena and events, which subsequently requires experimental verification.

In addition to the above definition, the term “hypothesis” means:

Probabilistic knowledge, explanation, understanding;

Option explanation for insufficient information;

Tentative explanation of cause-and-effect relationships and behavior;

A scientific assumption or assumption whose true meaning is uncertain;

An a priori, intuitive assumption about the possible properties, structure, parameters, efficiency of the object or process under study.

Essentially, a hypothesis is an indicative explanation (by no means categorical) of the cause-and-effect relationships of the object under study. This is a kind of form of transition from unstudied facts to laws and regularities, allowing the use of a hypothesis as a necessary tool for almost every scientific study of various objects, including control systems.

Each of the hypotheses, accepted, as a rule, on the basis of experience, intuition and available preliminary information, in most cases can be an expression of the initial focus of research on achieving certain goals.

This allows researchers to concentrate their efforts on the most promising and effective areas and to a certain extent reduce the consumption of resources for research work.

Hypotheses differ from ordinary guesses and assumptions in that they are adopted based on an analysis of available reliable information and compliance with certain scientific criteria.

In general terms, the hypothesis can be considered:

As part of a scientific theory;

As a scientific assumption requiring subsequent experimental verification.

The first group of hypotheses is part basic research, and the second is applied.

According to its hierarchical significance, a hypothesis can be general; if necessary, it is structured into auxiliary hypotheses of other levels.

The general hypothesis is associated, as a rule, with the main research question, its target setting, and auxiliary ones relate to lower-level tasks.

Depending on the breadth of use, hypotheses can be universal or specific. The first applies to all cases without exception. If confirmed, they can develop into theories and have a great impact on the development of science. Their development is based on many particular hypotheses that provide tentative explanations for specific individual phenomena.

The most commonly used hypotheses of this nature include statistical, probabilistic and the like.

According to the degree of validity, hypotheses can be primary (these are a kind of first options that serve as the basis for the development of more substantiated hypotheses), and secondary, which are put forward if necessary instead of primary ones, which is largely due to the refutation of primary empirical data.

In socio-economic systems, the explanation of individual phenomena and facts at the initial stages of research is often carried out in different ways, that is, several hypotheses are developed simultaneously, which are called working hypotheses or versions.

The concept of “working hypothesis” is a preliminary assumption put forward to initial stage research and serving only as a primary conditional explanation of the phenomenon under study.

Subsequently, as the above-mentioned conditional explanations are clarified and knowledge is obtained using working hypotheses, they come to accept a specific hypothesis.

Hypotheses help minimize the use of resources to achieve research goals.

They allow researchers to concentrate their efforts on the primary areas of knowledge and study of control systems.

When conducting a SU study, hypotheses can be accepted in relation to the following:

The target result of the effectiveness of the management system and the entire socio-economic system of the organization;

Properties of CS (entities and structure, methodology, functioning and development) and their limitations;

Relationship between the control system and the external environment;

Attitudes in the internal environment of the SU;

The relationship of the control system with the production system of the socio-economic system of the organization;

Elements and construction of subsystems and the control system as a whole;

The composition of factors, causes and their influence on the results of the functioning of the control system.

Options for conducting experiments and improving the control system.

Requirements for hypotheses

When studying control systems, requirements are imposed on the hypotheses used, the main ones of which are given below:

1. Purposefulness, providing an explanation of all factors characterizing the problem being solved.

2. Relevance (appropriate), i.e., reliance on facts, ensuring the admissibility of recognizing a hypothesis both in science and in practice. If a hypothesis does not use facts, then it is called irrelevant.

3. Prognostic ability, providing prediction of research results.

4. Testability, which makes it possible in principle to test a hypothesis empirically based on observations or experiments. This should provide either its refutation (falsifiability) and confirmation (verifiability). However, it cannot be said that all hypotheses are testable. There are: firstly, hypotheses that cannot be verified at the present time due to the imperfection of technical means, laws and regularities that have not yet been discovered, etc.; secondly, hypotheses are fundamentally untestable based on facts; thirdly, universal mathematical hypotheses related to abstract objects of research and not allowing empirical confirmation.

5. Consistency, achieved by the logical consistency of all structural components of the hypothesis.

6. Compatibility, ensuring the connection of the proposed assumptions with existing scientific theoretical and practical knowledge.
In case of incompatibility and contradictions between the put forward hypothesis and existing knowledge, it is necessary to check the laws and facts on which the hypothesis in question and previous knowledge are based.

7. Potential, including the possibility of using a hypothesis on the quantity and quality of deductive conclusions and consequences, their strength and influence on the development of system management.

8. Simplicity, based on consistency and a smaller number of initial premises contained in the hypothesis to obtain conclusions and consequences, as well as on a sufficiently large number of facts explained by it. In this case, the hypothesis can at the same time be of a more general nature. The simplicity of the hypothesis, of course, cannot exclude the use of complex mathematical apparatus to confirm it.

This raises many questions related to the confirmation or refutation of hypotheses. However, the most important criterion for one or the other, i.e. The truth of a hypothesis is still its empirical verifiability.

Obviously, there is a complete opposite between confirming and disproving a hypothesis. However, if the meaning of confirmation is, as a rule, relatively temporary, then refutation is final.

Moreover, to refute it, a deductive substantiation of the falsity of just one consequence of the hypothesis is sufficient. It is unlawful to confirm its truth based on the evidence of part of the statements. In the latter case, the conclusion is made using the inductive method.