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Statistical Aspects of a Research Project

Statistical Aspects of a Research Project. Mohd Ridzwan Abd Halim Jabatan Sains Tanaman Universiti Putra Malaysia. Outline. What, why and how The need for statistics Two types of study Decriptive Hypothesis testing Treatments, Experimental units and Replications

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Statistical Aspects of a Research Project

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  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

  14. Descriptive studies • Must have sampling (random, systematic, stratified) • Adequate replications • Representative

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

  16. 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!!

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

  18. STEP 3 • Determine the number of REPLICATIONS = the number of experimental units in one treatment

  19. STEP 4 • Determine the EXPERIMENTAL DESIGN = how you allocate the treatments to the experimental units

  20. 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

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

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

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

  24. + Vita control Problem = NOT RANDOMIZED + Vita control + Vita control

  25. Replication √ control +vita Randomization √ +vita control control +vita

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

  27. Replication • Reps are repetition of experimental unit • Sample in an experimental unit are not replications

  28. Four basic elements in experiments • Treatments • Experimental Unit • Replication • Avoiding bias = Randomization

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

  30. t test vs F test (ANOVA) • t test = comparing 2 treatments • F test (ANOVA) = comparing 2 or > 2 treatments

  31. Ladang A Ladang B Ladang C Paired t test 4.0 4.5 Randomized Complete Block Design (RCBD) 5.6 5.9 Two-way ANOVA 3.3 5.2

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

  33. Comparison between treatment means • LSD (least significant difference) =0.12

  34. 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

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

  36. ANOVA RCBD

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

  38. 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

  39. Treatment Combinations 12 TREATMENTS X 4 REPS = 48 PLOTS

  40. Allocate treatments randomly if CRD

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