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Classification of the hypothesis testing reliability criteria in socio-economic research

The article presents a methodology for assessing the acceptability of a hypothesis tested by a researcher based on statistical criteria applicable to the study results. The generally accepted criteria of reliability and power obtained by studying combinations of quantities of results recognised as true or false have been expanded by including the completeness criterion in the methodology. This criterion is obtained by supplementing the classification of research results with an area of undefined values. In addition to the general indicators of the reliability of observations (reliability, power and completeness), intermediate indicators of the data structure are proposed to clarify the results structure of the tests carried out. The universality of the model considered in work allows it to be applied in various socio-economic and other studies. The calculations results based on the model can be evaluated and used for decision-making, depending on the area and purpose of a particular study.


INTRODUCTION
In socio-economic, sociological and other areas of research, it is often necessary to confirm the validity of the hypothesis put forward by the researcher. Separate scientific and practical works, which have specialised areas, are devoted to the evaluating criteria methods. In [7], Ivashchenko E.N., Badanova A.A. are devoted to the issues of constructing criteria for assessing knowledge [8]. The importance of identifying information criteria for the audit of hypotheses is recognised not only for socio-economic matters but also for other areas, for example, chemical and technological systems [9].

RESEARCH METHODOLOGY
This study aims to develop a methodology for evaluating a hypothesis test by clarifying the classification of statistical criteria for such an assessment. Quantitative, well-formalised methods will allow the use of computer technologies for processing test results, which is especially important in the era of the digital economy. Specialists of information panels development of risk management, such as Bunting R. F Jr, Siegal D. emphasise: "Identifying the most powerful and informative measures, designing the most appropriate dashboards, and incorporating visual best practices are crucial steps required for evaluating the effectiveness and value of an enterprise risk management program» [10]. Domestic experts Shenderey P.E. and Shenderey E.E. also note the trend of automation of social research: "In recent years, the machine method of evaluating various sociological parameters has been increasingly used in sociology" [6].
The study stages include presenting generally accepted criteria for testing the hypothesis, the addition of these criteria and thus the expansion of the classification model, and the approbation of the model on empirical data.
The methods used include general scientific methods (analysis and synthesis, abstraction and concretisation, modelling) and special methods (grouping, arithmetic).
Model numerical values were used as analytical data.

RESEARCH RESULTS
The basis for checking the reliability of a hypothesis made by the researcher is percentage criteria of compliance level of the assumptions about the phenomenon nature (the hypothesis provisions a priori) and test results (the hypothesis conditions a posteriori). Such percentage criteria are reliability and power. In general, the reliability of hypothesis testing (criterion reliability) is the percentage of true (positive) values recognised as true by the test results. The hypothesis testing power (criterion power) is the percentage of false (negative) values recognised as false by the test results. True or false values are understood as values, respectively, confirming or not confirming the hypothesis (or having the opposite content for the hypothesis). At the same time, such a method of assessing reliability assumes that the researcher has information about the deemed true values, which is usually expressed in the form of some statistical distribution of the values of the studied quantity, for which the hypothesis is checked (distribution). In this case, the test results are compared with the statistical distribution. However, the    According to the model example, the reliability of the hypothesis is 70%, even though the true values are 4% more. The power of the hypothesis is 20%, even though there are 6% more false values. The cumulative reliability of the research results is calculated as the sum of reliability and power 70%+20% and is equal to 90%. Incorrectly predicted results are 100%-90% or 10%.
At the same time, in practical work, especially in socio-economic research, a situation may arise when the results of a study on any observation are missing or unreliable, for example, the respondent refused to answer the question. In this case, in addition to true and false values, the third type of value arises -"indefinite" or "absent"). Therefore, it is necessary to expand the classification of criteria for the reliability of the hypothesis, adding a zone of uncertain values to it. Such an extended classification is presented in table 3. The presence or absence of uncertain values (the "grey zone" in Table 3) characterises the degree of incompleteness or completeness of the criterion (hypothesis). The presence of uncertain values at the hypothesis level means that the hypothesis itself assumes some level of inaccuracy in data classification. Uncertain values at the results level in the absence of such values in the hypothesis means an incomplete collection of information during the study. The completeness of the study is achieved in the absence of values not defined during the study. Table 4 presents a model calculation of the data structure for assessing the criterion's reliability, power, and completeness. Such data can be, for example, the results of past and current sociological research of selfassessment of the level of material security of households (where True is the recognition of material security, uncertainty is the absence of an answer or the option "I find it difficult to answer", False is the recognition of insufficient material security). Table 4 show that the reliability of the hypothesis according to the test results is 65%, with a total share of true values of 74%. The power of the hypothesis is 9% with a total share of false values of 20%. The incompleteness of the hypothesis classification (uncertainty) is 2%, with a total number of actually undefined values of 6%. The final accuracy of the hypothesis predictions (cumulative reliability) is equal to 65%+9%+2%=76%. Incorrectly predicted results are 100%-76% or 24%.

The model data in
In addition to the generalised indicators of the reliability of observations (reliability, power and completeness), intermediate indicators of the data structure can be calculated to clarify the picture of the test results obtained. Such indicators characterise the ratio of other values of cells in Table 4 that were not used in early calculations. The following coefficients can be selected as intermediate indicators of the data structure (the calculation procedure based on the model data in Table 4 is shown in parentheses): -the confirmation coefficient if the hypothesis is True (74%/71%=1.04 with rounding); -the coefficient of confirmation of the hypothesis falsity (20%/16% =1.25); -the coefficient of confirmation of the hypothesis uncertainty (6%/13%=0.46 with rounding).
Thus, according to the model data, the true values turned out to be 4% more than expected, false ones -25% more than expected, undefined values -54% less than expected (that is, the results of the study turned out to be If it is necessary to detail the results analysis further, the ratio indicators of individual cells in Table 4 can be calculated in pairs. Such indicators will demonstrate how many times one value (numerator) differs from another (denominator).

CONCLUSIONS
Thus, as a result of the study, a universal model for evaluating hypotheses was obtained, applicable in various socio-economic and other studies. The author expanded the classification of criteria for the reliability of the hypothesis, supplemented by criteria of uncertain values. The calculations carried out using the model are quantifiable measurable and can be used to justify management decisions.