1. VARIATION, VARIABLE & DATA POSTGRADUATE METHODOLOGY COURSE Hairul Hafiz Mahsol Institute for Tropical Biology & Conservation School of Science & Technology

2. Introduction Scientific etiquette demands that a field be defined before study is begun

3. Variation n Definition: Variation is deviation of measurements or counts from the “average” among the subject under study. n Variation is the root cause of uncertainty in a quantitative study –For example, causes of uncertainty include physiological differences among participants, physiological state of a given participant, errors in measurement, mistakes in interpretation of patient’s response, the choice of who to include in the study, etc. –All of these are sources of variation in the data.

4. n Variation is a central concept in biometry, and statistics in general. n Here is a non-exhaustive list of potential causes of variation in any biological study. –Population variation : n Since no two individuals in any biological sample are likely to have identical genes and life-time environmental exposures, ages, etc. we expect, and observe, that there are consistent differences among individuals.

5. –Individual variation : n Individuals themselves may vary over time. For example, suppose we were measuring the weights of wolves in a pack. Our measurements would depend in part on when the animal last ate. –Sample variation : n Variation is caused by who we choose to study in the first place. For example, if we had chosen 279 other men as their sample, they almost certainly would not have obtained exactly the same results. –Observer or instrumentation error : n Mistakes made in measurement also produce variation. For example, some older participants in study may have incorrectly reported their hair-loss at age 30 because their memories were inaccurate.

6. Bias n Variation that causes our sample to consistently misrepresent the population of interest is called bias or systematic error. n Two of the most common sources of bias are the following: –Sample bias. The sample chosen does not represent the population. The phrase random sample means that the sample is unbiased; i.e., no consistent inaccuracy is introduced by the method of choosing a sample. –Instrumentation or observer bias. Such bias occurs when the instrument making the measurement is out of calibration, or the observer tends to read the instrument incorrectly, etc.

7. n Natural variation –all those events that happen in animate & inanimate nature not under the direct control of the investigator n Scientific study –concern with commonly accepted criteria of validity of scientific evidence. –Objectivity in presenting & evaluating data & the general ethical code of scientific methodology must constantly be in evidence

8. n Data –types of biological observations consist of numerical facts –1 numerical fact = datum n Variable –the actual property measured by the individual observations –synonymously use as character –single reading, score or observation of a given variable = Variate

9. Variables in Biology n 3 classes of variable : –Measurement, –Ranked & –Attribute n Measurement variable n differing states can be expressed in a numerically ordered fashion n 2 types of measurement variables –Continuous variables –Discontinuous variables

10. –Continuous variables n an infinite number of values between any two fixed points n Ex:length, area, volume, weight, angle, tempreture, period of time, percentage & rate –Discontinous variables n known as : meristic variables or discrete variables n variables that have only certain fixed numerical values, with no intermediate values possible n Ex: no. of colonies ext

11. n Ranked variables –the variables that cannot be measured but at least can be ordered or ranked by their magnitude n Attribute –variables that cannot be measured but must be expressed qualitatively –also known as : categorical or nominal variables –can be treated statistically when such attributes are combined with frequencies –change into tables that suitable for statistical analysis = enumeration data

12. n Derived variable –based on 2 or more independently measured variables whose relations are expressed in a certain way –Ratio = a single value the relation between 2 variables –Rates = the amount of a substance liberated per unit

13. Types of Biological Data / Measurement n Data on a Ratio Scale –measurement scales that have a constant interval size & a true zero –ex: no. of leaves per plant n Data on an Interval Scale –measurement scales that possess a constant interval but not a true zero –temperature scales

14. n Data on an Ordinal Scale –the preceding paragraphs on ratio and interval scales of measurement discussed data between which we know numerical differences –relative magnitudes are known n Data on an Nominal Scale –the variable may be called an attribute –data classified by some quality it possess rather than by numerical measurement –ex : genetic phenotypes

15. n Continuous & Discrete Data –any conceivable value within any observe range like plant height = continuous data –If we are dealing with a variable that can take on only certain values like the no. of leaves on a plant = discrete @ discontinuous variable @ meristic data

16. HIGH CLASSIFICATION of data/variable n QUANTITATIVE –one that can be measured in usual sense –convey information regarding amount n QUALITATIVE –not capable of being measure in the sense –can be categorized only –regarding attribute –manipulate numbers - frequencies

17. n Source of the data : –Primary data n basic @ original data –Secondary data n modified data from its original @ second data

18. How to get these data? –Routinely kept records n Any organization does keep records n Such as medical record - information on patients –Surveys n If no record - logical source may be a survey n Questionnaire –Experiments n Frequently the data needed to answer a question are available only as the result of an experiment. –External sources n In form of published report, commercially available data banks or the research literature