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Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
Introduction to experimental design
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Introduction to experimental design

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  • 1. EXPERIMENTAL DESIGN Dr. Rasha Aly Elsayed1 & Dr. Sanaa Abd Eltawab2 1Al Azhar University 2Beni Suef University2nd Lecture
  • 2. Intended learning outcomes2  Define statistics.  Define Experimental Design.  Know the Importance of Experimental Design.  Identify the Relationships between Experimental Design and Statistics.  Identify Some Myths about Experimental Design.  Briefly Describe the Costs of Poor Experimental design.  Steps in good experimental design  Goals of Experimental Design. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 3. Biological research involves data!!3 1) Collecting Data  Experimental Design 2) Summarizing Data  Simple numerical and graphical descriptions 3) Analyzing Data  Formal statistical methods for hypothesis testing and estimation 4) Communicating Results  Discussion and Interpretation Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 4. What is statistics?4  Statistics: A collection of procedures and processes to enable researchers in the unbiased pursuit of Knowledge. .‫مجموعة من الطرق والعمليات تمكن الباحثين من السعى وراء المعلومه بال تحيز‬  Statistics is an important part of the Scientific Method. State a Hypothesis Interpret the Design a Results—Draw Study and Conclusions Collect Data Analyze the Data Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 5. What is statistics?5 the core help from the statistician is in the design of the experiment Help with selecting conditions that relate to the objectives of the study Selecting the Experimental Units Deciding when REPLICATIONS exist Determining the ORDER in which the experiment is to be carried out THE DESIGN OF THE EXPERIMENT IS CRITICAL Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 6. What Is Experimental Design?6 Experimental design is the part of statistics that happens before you carry out an experiment. Science answers questions with experiments. Efficient and Effective Experiments Maximizing Information with Limited Resources. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 7. What Is Experimental Design?7  Biological insight!  Logic  Common sense  Planning  Requires an appreciation of statistics Note that there are different approaches to Experimental Design. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 8. Experimental Design and Statistics8  Good experimental design is about more than statistics.  You MUST know how you will analyse your experiment before you collect a single datum!  Once you have designed your experiment seek advice on the statistical test you will use.  Go ahead and use experienced people in your lab or department and/or a expert in statistics for this. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 9. Some myths about ExperimentalDesign9  Myth 1 Its better to spend time collecting data than sitting around thinking about collecting data, just get on with it.  Reality A well designed experiment will save you tons of time. This belief often results in staff and post-docs sitting around while supervisors rewrite grant proposals and permit applications Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 10. Some myths about Experimental Design10  Myth 2 “It does not matter how you collect your data, there will always be a statistical ‘fix’ that will allow you to analyze them”.  Reality NO! This belief results in people having lots of problems with their data. Big problems are non- independence and lack of control groups. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 11. Some myths about Experimental11 Design  Myth 3 “If you collect lots of data something interesting will come out and you will be able to detect even very subtle effects”  Reality NO! Generally collecting lots of data without a plan wastes your time and someone’s money. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 12. Costs of poor design12  Time is wasted This is something you can’t afford and its sometimes downright embarrassing.  Money and resources are wasted This is something your supervisor (or department or company) can’t afford and tends to make them quite angry. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 13. Costs of poor design13  Ethical issues (when animals or humans are experimental subjects) Experiments must minimize the stress and suffering of any animals involved. Minimum numbers must be used. Experiments must have a reasonable chance of success. Ethical issues include causing damage or excessive disturbance to an ecosystem. Using poor design in animal studies is not only wasteful and embarrassing but may also be illegal. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 14. Steps in good experimental design14 Three important steps in good experimental design: 1. Define the objectives: Record (i.e. write down) precisely what you want to test in an experiment. 2. Devise a strategy: Record precisely how you can achieve the objective. This includes thinking about the size and structure of the experiment - how many treatments? how many replicates? how will the results be analysed? 3. Set down all the operational details: How will the experiment be performed in practice? In what order will things be done? Should the treatments be randomized or follow a set structure? Can the experiment be done in a day? Will there be time for lunch? etc. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 15. Goals of Experimental Design15  I. Avoid experimental artifacts  II. Eliminate bias 1. Use a simultaneous control group 2. Randomization 3. Blinding  III. Reduce sampling error 1. Replication 2. Balance 3. Blocking Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 16. I. Experimental Artifacts16  Experimental artifacts: a bias in ‫انحياز‬ a measurement produced by unintended ‫مقصود‬ ‫غير‬ consequences of experimental procedures.  Conduct your experiments under as natural of conditions as possible to avoid artifacts. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 17. II. Eliminate bias: 1. Control Group17  A control group is a group of subjects left untreated for the treatment of interest but otherwise experiencing the same conditions as the treated subjects.  Example: one group of patients is given an inert placebo (inert medication). Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 18. II. Eliminate bias: The Placebo Effect18  Patients treated with placebos, including sugar pills, often report improvement.  Example: up to 40% of patients with chronic back pain report improvement when treated with a placebo.  Even “sham surgeries” can have a positive effect. This is why you need a control group! Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 19. II. Eliminate bias: 2. Randomization19  Randomization is the random assignment of treatments to units in an experimental study.  Breaks the association between potential confounding variables and the explanatory variables. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 20. II. Eliminate bias: 3. Blinding20  Blinding is the concealment of information ‫اخفاء‬ from the participants and/or researchers about which subjects are receiving which treatments.  Single blind: subjects are unaware of treatments.  Double blind: subjects and researchers are unaware of treatments. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 21. II. Eliminate bias: 3. Blinding21  Example: testing heart medication  Two treatments: drug and placebo  Single blind: the patients don’t know which group they are in, but the doctors do.  Double blind: neither the patients nor the doctors administering the drug know which group the patients are in. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 22. III. Reduce sampling: 1. Replication22 This is the number of experimental units measured for each treatment. Increasing the number of replications means collecting more information about the treatments.  Experimental unit: the individual unit to which treatments are assigned 2 Experimental Experiment 1 Units Pseudo replication 2 Experimental Experiment 2 Units Tank 1 Tank 2 8 Experimental Units Experiment 3 All separate tanks Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 23. III. Reduce sampling:1. Replication Why is pseudoreplication bad?23 Experiment 2 Tank 1 Tank 2  problem with confounding and replication!  Imagine that something strange happened, by chance, to tank 2 but not to tank 1  Example: light burns out  All four lizards in tank 2 would be smaller  You might then think that the difference was due to the treatment, but it’s actually just random chance Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 24. III. Reduce Sampling.1. Replication24 Why is replication good?  Consider the formula for standard error of the mean: s SE Y  n Larger n Smaller SE Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 25. III. Reduce sampling: 2. Balance25  In a balanced experimental design, all treatments have equal sample size. Better than Balanced Unbalanced  This maximizes power.  Also makes tests more robust to violating assumptions. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 26. III. Reduce sampling: 3. Blocking26  Blocking is the grouping of experimental units that have similar properties.  Within each block, treatments are randomly assigned to experimental treatments  Blocking allows you to remove extraneous variation from the data.  Like replicating the whole experiment multiple times, once in each block.  Paired design is an example of blocking. Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 27. III. Reduce sampling: 3. Blocking27 Experiments with 2 Factors  Factorial design – investigates all treatment combinations of two or more variables.  Factorial design allows us to test for interactions between treatment variables Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 28. III. Reduce sampling: 3. Blocking28 Factorial Design pH 5.5 6.5 7.5  An interaction betweenTemperature two (or more) explanatory 25 n=2 n=2 n=2 variables means that the 30 n=2 n=2 n=2 effect of one variable depends upon the state 35 n=2 n=2 n=2 of the other variable 40 n=2 n=2 n=2 Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab
  • 29. Thank You29 THANK YOU Dr. Rasha Elsayed & Dr. Sanaa Abd Eltawab

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