1. Statistical Aspects of a Research Project Mohd Ridzwan Abd Halim Jabatan Sains Tanaman Universiti Putra Malaysia

2. Outline  What, why and how  The need for statistics  Two types of study Decriptive Hypothesis testing  Treatments, Experimental units and Replications  Experimental Design and Analysis

3. Starting a Research Project  What?  Why?  How?

4. WHAT?  What is the objective?  What do you want to find out?  What is the solution to the problem?

5. WHY?  Why do you want to study that?  Is it new?  Is it a problem?  Is it important?  Can you do it?

6. WHAT?  Usually your supervisor will tell or guide you  You can also suggest your own

7. WHY?  You must SEARCH, READ, ASK and obtain information*  FIND OUT what others have done  You must be CONVINCED that it is IMPORTANT to know

8. HOW?  How can you find the answers?  Experiments?  Treatments?  Statistical Methods?

9. Why do we need to use Statistical Methods?  Makes results of study valid and acceptable  Helps in deriving conclusions from results  Provides degree of confidence in the conclusion made

10. What happens if you don’t use statistical methods  Your results will not be accepted  You cannot make a valid conclusion  You cannot answer any question

11. What you need to do  Determine what you want to find out = OBJECTIVE/S  READ and understand the topic = LITERATURE REVIEW, JUSTIFICATION  Determine what you must do = MATERIALS AND METHODS

12. MATERIALS & METHODS  How you conduct the study  Two types of study: Descriptive Hypothesis testing  Must include the statistical method!

13. DESCRIPTIVE STUDY  Getting new basic information  e.g. a new crop variety, a survey  No comparisons  No hypothesis  Descriptive statistics – mean, SD, frequency distribution

16. Descriptive studies  Must have sampling (random, systematic, stratified)  Adequate replications  Representative

17. Hypothesis testing  Comparing between treatments  Treatments designed to meet objectives  Must have an experimental design

18. STEP 1  Determine your treatments: fertilizer? variety? hormone? Method?  Are you studying ONE factor only – SIMPLEST  Are you studying 2 factors – FACTORIAL experiment – more difficult  Are you studying 3 factors – DON’T!!

19. STEP 2  Determine your EXPERIMENTAL UNIT = the smallest unit that you apply your treatment  One pot?  One plot?  One plant?  One animal?

25. STEP 3  Determine the number of REPLICATIONS = the number of experimental units in one treatment

26. STEP 4  Determine the EXPERIMENTAL DESIGN = how you allocate the treatments to the experimental units

27. CRD vs RCBD  To BLOCK or NOT TO BLOCK??  If experimental units are HOMOGENEOUS = don’t need blocking = CRD  If experimental units are HETEROGENOUS = need BLOCKING = RCBD

28. BLOCKING  Group experimental units that are similar  Number of units in one block = number of treatments

29. RANDOMIZATION  Treatments must be randomized – to avoid bias  You cannot have any influence which treatment goes to which unit

30. + Vita control Comparison of padi yields with and without Vita Problem = NO REPLICATION

31. + Vita control Problem = NOT RANDOMIZED

32. +vita control Replication √ Randomization √

33. + Vita control + vita OK or not? Problem – sampling unit treated as exp. unit! No replication!

34. Replication  Reps are repetition of experimental unit  Sample in an experimental unit are not replications

35. Four basic elements in experiments  Treatments  Experimental Unit  Replication  Avoiding bias = Randomization

36. +vita 7.8 t Control 6.3 t Control 7.2 t Control 6.9 t +vita 7.9 t +vita 8.1 t Homogeneous units Independent t test One-way ANOVA Completely Randomized Design (CRD)

37. t test vs F test (ANOVA)  t test = comparing 2 treatments  F test (ANOVA) = comparing 2 or > 2 treatments

39. Ladang A Ladang B Ladang C Paired t test Randomized Complete Block Design (RCBD) Two-way ANOVA 4.5 4.0 5.65.9 5.2 3.3

40. COMPLETELY RANDOMIZED DESIGN (CRD) 3 treatments 4 reps Homogeneous units ONE-WAY ANOVA

41. T1T2T3 4.23.54.9 3.93.35.1 4.13.84.7 4.43.05.3 Min4.153.405.00

42. SourcedfSSMSF Treatment25.132.5736.72** Error90.670.07 Total115.80

43. Comparison between treatment means  LSD (least significant difference) Min T35.3a T14.4b T23.0c =0.12

44. Program dengan SAS  Data varieti;  Input trt hasil;  Cards;  T1 4.2  T1 3.9  Data  ;  Proc anova;  Class trt;  Model hasil=trt;  Means trt/lsd;  run

45. Blok A Blok B Blok C Blok D RANDOMIZED COMPLETE BLOCK DESIGN (RCBD)

46. ANOVA RCBD SourcedfSSMSF Treatmen t 2 Block3 Error6 Total11

47. Program SAS  Proc Anova;  Class trt blok;  Model hasil=trt blok;  Means trt blok/lsd;  Run;

48. FACTORIAL EXPERIMENTS  Looks at 2 or more factors in one experiment:  Example: Effects of variety – V1, V2, V3, V3 Effects of Irrigation – I1, I2, I3 4 x 3 factorial 12 treatment combinations

49. Treatment Combinations VARIETIES IRRIGATIONV1V2V3V4 I1V1I1V2I1V3I1V4I1 I2V1I2V2I2V3I2V4I2 I3V1I3V2I3V3I3V4I3 12 TREATMENTS X 4 REPS = 48 PLOTS

50. Allocate treatments randomly if CRD

51. Sourcedf Variety (V)3 Irrigation (I)2 V x I6 Error Total47 Main effects Interaction ANOVA FOR CRD FACTORIAL

52. Block 1 Block 2 Block 3 RCBD FACTORIAL 12 treatments randomized in each block Block 4

53. Sourcedf Block3 Variety (V)3 Irrigation (I)2 V x I6 Error Total47

54. SPLIT-PLOT EXPERIMENT  Two or more factors  The factors use unequal plot size  Use only when necessary

55. V3V4V1V2 I2I1I3 I2I1 I3I2 Main Plot Sub Plot Block 1 Block 2 Block 3 Block 4

56. Source df Block (B) Irrigation (I) B x I ( error A ) Variety (V) V x I Error (B) Total ANOVA FOR SPLIT PLOT

57. Make a checklist  Treatments = refer to objectives  Experimental unit  No of replications  Design = randomization  Statistical test

58. TERIMA KASIH