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Logical thinking in scientifc work and most frequent errors from idea to publication Prof. dr. sc. Mladen Petrovečki Ph.D. Postgraduate Study Zagreb University.

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Presentation on theme: "Logical thinking in scientifc work and most frequent errors from idea to publication Prof. dr. sc. Mladen Petrovečki Ph.D. Postgraduate Study Zagreb University."— Presentation transcript:

1 Logical thinking in scientifc work and most frequent errors from idea to publication Prof. dr. sc. Mladen Petrovečki Ph.D. Postgraduate Study Zagreb University School of Medicine 2007/08

2 1. Typing error from idea to publication Prof. dr. sc. Mladen Petrovečki Ph.D. Postgraduate Study Zagreb University School of Medicine 2007/08 Logical thinking in scientific work and most frequent errors

3 2. Logic, reasoning

4 Logic of scientific work Mirko Jakić. Logika. Školska knjiga, Zagreb rules of logic and logic itself as a way of valid thinking is more expressed in science and philosophy compared to other human activities… 2.science is recognized by utilizing empirical methods and therefore logic is prerequisite in scientific methodology...

5 3.use of is logic evident in using logical reasoning, by using terms such as rules, conclusions, definitions, distributions, proves, etc. 4.logic – how our thinking is valid in our mission to find the truth… Logic of scientific work

6 Logic of “scientific” work

7 Types of logic

8 3. Unscientific procedures diligence (habit, attitude, manner, believe, momentum) authority intuition

9 More unscientific procedures

10 4. Argument, proof

11 5. Research logic system models of the system deterministic probabilistic event probability  p(E) 0  p(E)  1

12 6. Probability Machiavelli za početnike. Jesenki & Turk, Zagreb

13 Probability, a term mathematical calculation that something, event, will occur mathematic  probability theory statistics mathematics scientific methodology logic, philosophy reasoning about event feasibleness

14 Probability, calculation symbol – P No. of expected events P = No. of all events values range 0 – 1: 0 – impossible event 1 – certain event

15 Probability vs. fortune

16 Probability vs. fortune II

17 Probability vs. coincidence

18 Probability vs. impossibility

19 Probability, the term probability vjerojatnost, mogućnost possibility mogućnost, vjerojatnost, izvedivost likelihood vjerojatnost, mogućnost chance mogućnost, prigoda, slučaj, slučajnost, vjerojatnost, sreća, povoljna prilika odds izgled, prednost, vjerojatnost, slučajnost

20 probability calculation probabilistic model of the system 7. Statistics

21 Lord Kelvin ( ) James C. Maxwell ( ) Ludwig Boltzmann ( ) Willard Gibbs ( ) p = ? Statistical mechanics

22 Statistical asimmetry 10A : 10B  combinations p = 5,56x10 -6

23 1 : Statistical mechanics

24 Second low of thermodynamics closed system:  entropy const. entropy disorder (chaos) in closed system is constant or  A. Šiber: Red i nered as.html

25 population variable knowledge about population      Measuring & 9. Research

26 10. Variable all variables in research as many of them the end of research simple  complex (data) accuracy (numbers) measuring scales

27 11. Measuring scales RATIO ORDINAL NOMINAL INTERVAL

28 12. Error systematic incidental

29 13. Population populationvariable samplestatistical data analysis knowledge on population SAMPLING knowlede on sample

30 14. Sample part of population what? who? when? where? size

31 Sample representative able to be measured probabilistic simple system stratified cluster

32 Sample unrelated related

33 15. Sampling

34 Sampling MedCalc

35 16. Bias (sampling)

36 Bias – systemic sampling error prevalence bias (Neyman) admittance rate bias (Berkson) answering rate bias etc. Bias (sampling)

37 17. Blinding single-blind double-blind triple-blind quadruple-blind

38 Bias, blinding

39 18. Control must have to be compared with experimental group Hawthorn effect research with no control group subject changes behavior with a knowledge that is a part of experiment subject feels better with knowledge to be a part of experiment

40 19. Hypothesis

41 20. Statistical hypothesis uelemental statement utruth or not (false, lie) uhypothesis testing  finding the truth Ivana Brlić Mažuranić Kako je Potjeh tražio istinu Mladost, Zagreb; Albert Kinert, 1967.

42 Statistical hypothesis utruth  real object state probabilistic system: truth  probability usignificant  any occasion other that accidentally: probability  level of significance

43 21. Null-hypothesis No difference

44 22. Testing the hypothesis A.null-hypothesis B.statistical test C.level of significance D.statistics calculation E.conclusion

45 A. Hypothesis null – H 0 – no difference alternate – H 1 – difference exists only one can be truthful only one can be accepted, other will be rejected

46 B. Choosing the test measuring scales sample size related on unrelated samples data distribution parametric nonparametric no. of variables etc.

47 23. Statistical tests ScaleOne sampleTwoThree or more relatedunrelatedrelatedunrelated NominalbinomialMcNemarCohran chi-squareFisherchi-sqr. chi-square/ OrdinalKol.-Smirn.WilcoxonFriedman MWp/median MosesKW Interval... Ratio...

48 Paired & unpaired tests

49 C. Level of significance P   if defined before statistics   – probability of rejecting H 0 when H 0 = is truth  -error (type I error or false positive error) as less as possible default values, e.g. P<0,05

50 24. Statistical errors

51 D. Statistics computation... P = exact value three decimals P > 0,05

52 25. Software

53 26. Concluding (E) low P  low possibility to reject the truth conclusion: P <  low probability that H 0 is true reject (not accept) null hypothesis accept alternate hypothesis statement “...” is truth with P =...

54 27. Yes & No in statistics hypothesis = ? calculation = ? correct data = ? all conditions for statistic valid = ? no limitations = ?

55 Example 1: “not” in correlation x y x y x y

56 Nrp linear 1180,250,006 logarithm1180,43<0,001 Example 1, cont.

57 lecturesstudentsstudents qualityZagrebother well10 31 bad 0 19 total1050 Example 2: “not” with  2 -test

58 Lupus 2004;14:426 Example 3: another “not”

59 ne valja / valja Lancet 2007;370:1490 Example 4: “not” in graphs

60 28. E-help

61 29. Significance vs. accuracy

62 30. The truth

63 Literature Marušić M., ed. Principles of Research in Medicine 1st ed. in English Zagreb Medicinska naklada, 2008.

64 Prof. dr. Mladen Petrovečki Katedra za medicinsku informatiku Medicinski fakultet Sveučilišta u Rijeci Odjel za imunološke pretrage Klinički zavod za laboratorijsku dijagnostiku Klinička bolnica “Dubrava”, Zagreb 


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