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A brief preface attacks modern scientific methods and icons of science such as Newton and Einstein. The scope of the essay is made. Chapter 1 suggests that gestalt observation of the problem is the best way to get undistorted test results. It is virtually impossible not to distort data results when testing outside of the real world inertial framework, except for mathematics and logic problems. There are always unknowns that cannot be accounted for and that will alter data results outside of the domain of the hypothesis under examination. Chapter 2 shows forced set method. Try and whittle the subjects or objects of the data set to get what you want. Limiting the set cuts back on expense in money and time. If the most opportune set will not confirm your results, drop the hypothesis. A small successful result will offer some opportunity to recover expenses in for profit industry, and validate pure research. Once the core sample is successful within acceptable limits, study the similarities and differences minutely. Surround the core in a bullseye fashion with rings of data from more successful to less. (For example, if the core is 99% successful, and the outer sample 10% successful, with the utmost virtually 0% successful, use the core sample analysis to determine why these samples are not up to par.) By going from known to unknown, you may be able to find the unknowns that turn 10% success to 99% success. Nobody wants to be wrong. Force the data set to get what you want right off the bat. This is not bad science but good economy of effort.

Detalhes do Produto

    • Formato:  ePub
    • Origem:  IMPORTADO
    • Editora: SMASHWORD
    • Assunto: Filosofia
    • Idioma: INGLÊS
    • Ano de Edição: 2017
    • Ano:  2017
    • País de Produção: Canada
    • Código de Barras:  2001121031766
    • ISBN:  9781370162604

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