Solving a Biological Problem

Summary

The chapter is about the methodology that is involved in exploring some understanding toward solving a biological problem while doing research in quest. With citation of example of malaria as a problem along with historical stage involved towards gradual understanding related to its causes leading to the findings to its cure and preventions, an attempt was made to present the approach that has been involved in addressing the scientific issues in past.

A practical approach that is possible to conduct practically at school level to get the students introduced with research at elementary level and built up some interest in students has been presented in the following chapter.

Mathematical and statistical tools that are needed in research have been introduced to analyze the data obtained by using research methodology (hypothetical) to arrive on conclusions about certain aspects of the issues related to malaria to accept, discard or modify the hypothesis on a scientific query.

Introduction

The human being started putting efforts to explore the world around him right from very beginning. The history is full of examples that show that early human being somehow recorded their opinion about different thing. With passage of time, human beings learnt to evaluate the correctness of their thoughts and opinion on any scientific issue, by setting some experiments, drawing conclusion (hypothesis) based on results, verification of hypothesis by other experiments and reporting it. Research methodology is latest approach involving these procedures to address a scientific problem.

Steps involved in Biological Research Procedure

Identification of a problem

Sufferings of mankind that includes diseases, scarcity of food shelter, utilities etc or related matters are the existing problems from biological origin.

To initiate research in an area a problem in a particular area is identified. There can be a wide range of problems e.g. an approach to find a cure for newly emerging disease, find a more effective drug for a curable disease, increase the shelf life of a product etc.

Generation of hypothesis

Available understanding on an issue that is published in scientific journals is used to draw logical opinion, hypothesis, underlying the biological processes and indicate possibilities that can lead to the management of the problem. There are often more than one hypothesis available for a given scientific query. Usually one hypothesis is being testified by given experiments.

(a)Shelf life of chadder cheese increases with increase in number of yeast cells present into it.

(b) Quality of spinach deteriorates with increase in rate of transpiration.

(c) The rate of decrease in microbial population in a food sample in response of heat treatment at 600C is inversely proportional to its total organic content.

Laboratory safety Procedures

Every type of biological experimentation should comply with necessary safety procedures that ensures the safety of professionals involved in conducting the experiments and other people. That includes use of special clothing e.g. coats, gloves head cover spectacles etc. All the biological material is carried or grown in specially designed containers that ensure no risk for leakage that can be hazards for other people who can come in contact. All the biological material is heated at 121oC under pressure at 15 psi to kill all type of cells before discarding it whereas chemical agents are discarded as mentioned in safety manuals.

Experiment Design

Experimentation is a practical approach for investigation a scientific query leading to generation of observations called data. A same question can be investigated by using different experimental approaches. The selection of an experimental design mainly depends upon time required, extent of precision, availability of resources etc.

Each experiment design has got some limitations and it is important to ensure that the selected experiment design can optimize for investigating the required scientific query. Each experiment is design by usually changing a variable.

In order to test a given hypothesis, experiments are set, usually in triplicate and experiments are repeated at least three times to ensure reproducibility of the data. To avoid adding error in the data it is important to set negative and

positive control for a given experiment. Positive control has an ingredient instead of ingredient to be test that should positive result when is added in the reagents and processed as per standard method of experiment in given conditions.

On other hand Negative control is set same as positive control but has water or other solvent added instead of reagent to be tested.

It is very important that selection of sample that is used in the experiment should be random. It is important to ensure that all the experiments should be done at the same conditions. All those factors that can contribute to add errors should be taken care of.

Data Collection and analysis

Observations can be collected after completion of experiments or while they are in progress depending on nature of experiments. The data is not always a integral values but can be visual observations that can be recorded by photography. The data is usually recorded with appropriate units in tabular form. This is known as raw data. Every data has some error added into it.

Mathematic and statistics an integral part of Biological Analysis

The observations that are collected as numerical value after experiments comprise raw data.

Mathematical or statistical methods are used to minimize the effect of errors present into it the raw data. The most widely and commonly statistical method that is used to decrease error in data is finding an average in any given readings.

Beside that mathematical or statistical tools are used to deduce a logical ground based on numerical value to support, modify or discard any scientific opinion (hypothesis) that is built up on earlier reported scientific findings

Use of ratio proportion and percentage

Data is analyzed by using mathematical or statistical tools, most commonly that are used include ratio and percentage, for finding an effect of changing a variable on other parameters in a given set of condition.

If a numerical variable ‘a’ represents intensity of a biological event that needs to be compared with intensity of another similar event represented by numerical variable ‘b’ then ratios are taken out

Ratio between intensity of two biological events = a (equation 1) b

In order to represent this comparison on scale of 100, percentage of ratio between variables are taken out

The general formula for percentage is given as below

%= Ratio between two numerical variable x100(equation 2)

Substitute equation 1 in general formula of percentage as shown by equation 2

Percent % (ratio between intensity of two biological events) ={ a }x 100 (eq 3) b

Usually biological data can comprise of in a range of very small value to very large and hence it is important to convert it on Log10. The general formula for expressing any numerical variable is shown by equation 4 as follow

Number(N) on Log 10 scale= Log10(N) (equation 4)

Substitute equation 3 into equation 4

Log 10(ratio between intensity of two biological events) =Log10 { a x 100}(eq 5) b

Technical limitation associated with use of Log10 scale

While expressing any data on Log10 the difficulty arises when it is needed to express integral 0 on Log10 scale (Log10(0)=infinity). In order to cope with this difficulty 1 or any fraction of number that falls within permissible limit of error (<5%) is added in all the data in its untreated form. Since the value added is added in every observation and magnitude of value is very little, so it s effect is nullified when data is converted on Log10 scale. In later stages the data can easily be plotted on any type of graphs as discussed in later part of this chapter.

Significance of error in decision making and predictions on biological data

Every data collected has certain extent of error present into it depending upon experimentation design, procedures and method of taking observations.

This error needs to be evaluated before using the data for testing any hypothesis, decision making or predictions. It is defined as tentative variation on negative and positive scale in a set of observations from actual value.

The actual numerical value of a biological effect is (B). An experiment was done to note this biological effect. The experiment was repeated N times e.g. (N1 N2 N3 ………Nt) to showing observations B as (B1 B2 B3……….Bt) respectively.

The first stage in calculation the error is to find an average

The general formula for Average is

Average = sum of numerical values of individual observation eq 6

Number of times the observation was taken

Substitute the values in equation 6

Average ={B1+ B2+………….Bt}-------- eq 7 {Nt}

Sum of values of observations ={B1+ B2+………….Bt}is shown by S{B}

No of times the experiment was repeated {Nt} ={N}

Substitute the value in equations in equation 7

Average= S{B} equation 8 {N}

Lets value of Average be represented by X

Substitute in equation 7

Average = X

Second stage is to find the difference between each numerical value of observation and Average

= B1-X, B2-X------------Bt-X,

Third stage is to square each of the difference

=B1-X)2,(B2-X)2, (B3-X)2-----------(Bt-X)2

Fourth stage is to add the square of differences

=(B1-X)2+(B2-X)2------------------------+(Bt-X)2

Fifth stage is to divide square sum of difference of average of individual numerical variable with number of observations e.g N =S(B1-X)2,( B2-X)2,…..(Bt-X)2 N

Standard deviation or Error is obtained by taking square root of the quotient obtained by dividing the square sum of difference between the average and the individual numerical variable with number of observations.

=√S(B1-X)2,( B2-X)2,…..(Bt-X)2 N

It is an integral value that is expressed, as on negative as well as positive scale e.g if error calculated is 3 then it would be +3 as well as –3. It represents a range within which actual value may lie.

In olden days, such calculations were done with the help of calculator but now same work can be done more easily by using different software e.g. excel with computer as a tool.

It is often very different to understand the effect underlying in any biological data by merely looking at numerical values. The different types of graphs are used for visual presentation of effect by trends available in data

The most commonly types of graphs that are used for the presentation of data are given in figure 2.The same data for different sample e.g. A, B, C is presented on percentage scale by using horizontal and vertical bars whereas error is shown by error bars .Another set of data for sample D, E, F was presented by line graph.

Data analysis to decide the status of hypothesis

After the mathematical and statistical treatment of raw data a logical ground built up by comparing certain numerical values or more often represented on graphs to accept, modify or reject any hypothesis.

In order to test the hypothesis “The rate of decrease in microbial population in a food sample in response of heat treatment at 600C is inversely proportional to its total organic, 100 cells of Saccharomyces cerevisiae were inoculated in same volume in mediums with concentration as X, 3X and 70X respectively to make final concentration of each medium as 100 cells/ml.(Sample D contains 3X concentration medium, Sample E contains X concentration and Sample F contains 70X concentration). The medium were kept at 600C for 1x104hours and cells were assayed for viability after 10 hrs, 100, hours, 1000hrs and 10000 hrs respectively.

The viability being dependent value was plotted on a graph Y axis against the time in hours after expressing the value on Log 10 scale. Different data points taken on the graph are sufficiently scattered and can not be joined by a straight line accommodating all the point on it.

The trend line can be drawn manually by accommodating maximum number of points and leaving as many point above the line as many are there below that line. Such a manually drawn line can not be used for any type of scientific predictions. Otherwise highly precise trend line for a given data can be drawn as explained in section given in the end of the chapter that can be used for making decisions on a given hypothesis and for making predictions.

The error bars extending on positive as well as negative scale in same magnitude of Y scale is plotted against each observation.

The observation that were taken in this experiment are shown as graphs in Figure 2 e.g. Sample D, E and F were presented by using line graphs on Log 10 scale.

The trend lines are introduced to show the type of dependency of one parameter on another. As already discussed earlier the biological data is often presented on Log 10 scale to observe the effects in broader prospective and ignore the slight changes especially when study is made on larger populations of samples e.g. cells with a wide range of variations.

The data show that sample D and E has got similar trend (with an increase in variable on X axis, there is a decrease on Y axis variable, Y axis is dependent on x axis and is inversely proportional to it) whereas in sample F, Y variable is independent of X variable as no change in Y variable is observed with an increase in X variable and this is evident by a straight trend line.

The conclusion drawn as evident by three graphs that number of viable yeast cells were found to decrease with passage of time when concentration of organic content in medium was X in sample E or 3X in sample D that is represented by a slopes in respective graphs. But this effect was found to be lost when organic concentration was raised to 70X in sample F and that is.

This experiment supports the hypothesis “ The rate of decrease in microbial population in a food sample in response of heat treatment at 600C is inversely proportional to its total organic content”.

If reported literature indicate some other type of experiments done to check the same hypothesis then results are compared and reason of the variations if there are any are discussed with scientific reference and is reported in a scientific journal.

Research Methodology

Theory accepted

Modification in hypothesis

Publication in scientific Journals

Discarding above hypothesis

Supporting above hypothesis

Results interpretation

Different Hypotheses

Data analysis

Data collection

Experimental design for a given hypothesis

Literature reported in related area

Identification of Biological Problem

Interpretation of data

The data that is obtained is interpreted to draw scientific conclusions. The reported literature is used to give explanation of the conclusion drawn.

In the light of conclusion drawn either the hypothesis is accepted, rejected or is modified.

If the hypothesis is proven correct with experiments, then it is known as a theory. Scientific articles based on proven hypothesis e.g. theory, disapproved or modified hypothesis are written by scientist involved in research and are sent for publication in scientific journals.

Biological Problem as an example

A school teacher planned an excursion for students and took paramedic staff equipped with sample collection facility to country side area to study the prevalence of any non contiguous disease in human beings and animals. Malaria is an example of non contiguous disease that is spread only by infected female mosquito is malaria and is a matter of great concern.

As reported in newspapers and other literature there was a population of 100,000 people living with minimal facilities of life that on a huge natural water reservoir for drinking water. Recent heavy rain resulted in collection of water of stagnant water and much of this water in later stage drained in reservoir.

The students prepared a report explaining how did they use biological methodology to study this case.

a) Identification of problem

Prevalence of certain incidences of disease symptoms similar to malaria in a given locality was identified as a biological problem. Students wrote the following note after referring the literature about malaria to get basic information about disease. Please refer the picture given below and the literature cited in later part of this chapter.

From Mala aria ( bad air) to Malaria-over a period of time

Malaria has been a matter of concerns since very beginning. Understanding about malaria has changed over a period of time. Initially it was considered to be caused by some supernatural power. Supported with the findings of higher incidences near the marshy area, malaria was thought to be caused by bad air that is found near marshy areas. With the emergence of germ theory malaria by Louis Pasteur (1852-1895) was thought to be caused by any bacteria. It is strongly believed until Charles Louis Alphonse Laveron (1845-1922) during microscopy of Blood from infected patients observed causative agent of malaria in 1880 and later on reported it to Academy of Medicine in Paris. Albert Freeman Africanus King (1841-1940) who was associated with George Washington University on basis of supporting reports presented the idea that mosquitoes are the mean of transmission of malaria (vector) and discarded the opinion that bad air in marshy area has any role in causing malaria. On basis of his data he suggested that proper netting can help to control the incidences of malaria in Washington.

Ronald Ross (1857-1932) was a physician, who studied the life cycle of malarial parasite in mosquites e.g (different stages of parasite infection with a change in morphology) and its transfer to human beings and birds. In 1898 Giovanni Battista Grassi (1854-1925) on basis of understanding about malaria obtained by published literature set an experiment to infect a person who never had malaria (with his permission) in an area of Rome where there was not any case of malaria reported and mosquitoes were not found by exposing the person with Anopheles clavigar for ten night and later on patient developed the symptoms. On basis of his experiment he proved that malaria is spread by mosquitoes in human beings that carries the causative agent Plasmodium.

The complete cycle of P. falciparum was observed by Grassi Bignami and Bastianelli in 1899 and the work has been published by Grassi in 1900.

The life cycle of Plasmodium has three reproduction stages with different morphologies (shapes). The mosquitoes inject sporozoites in human being s skin that through blood goes to liver where they multiply and change into Merozoites.The second cycle of multiplication does into RBC. Some of the meroziotes after passing through reproductive cycle in RBC converts into gametocytes (male and female gemates) and enters in gut of mosquitoes when they suck blood from an infected human being where they undergo sexual reproduction to zygote which later on converts Oocyst. Oocyste after under going asexual reproduction burst to release newly formed sporozoites that enters in salivary gland of mosquitoes

At the site students made a survey of that area and noted the initial observation with the help of photography. They found that not only human being but also the birds are affected by the disease

Initial Findings

They found that the reservoir (a) was associated with dark places where high populations of mosquitoes can be seen (b).Beside that a few sick birds were also found resting on ground. Diseased patients report to an increase in high fever with shivering that stays for some times and then fever become low or even normal with sweating or even without it. These symptoms are repeated with intervals and patient feels weakness.

Initial findings support that the disease is malaria.

b) On the basis of their initial findings the following hypothesis was built up.

The disease may be Malaria and is caused by Plasmodium

Experimentation

Plasmodium infect the red blood cells. In order to established that diseased people are suffering from malaria at least 63 patients having disease were bled to collect blood samples. This type of sample that is under study is called as test sample. Blood were taken from at least 50 healthy who do not have any symptoms of disease. Since these people do not have any symptoms of disease it is very likely that Plasmodium may never be detected in their blood. Such a sample that is known to give a negative test is a called a negative control The blood and water samples were collected in collection tubes specially designed for this purpose.

The fixed slides of infected Red Blood Cell (RBC) were purchased from the market and was taken as positive control.

(a)The sample was not only collected from human beings but also from the diseased animals as well. That the blood of each sample was spread on a glass slide, fixed and stained with Giemsia and was observed under microscope.

(b)Beside that the blood samples from infected people were inoculated (added) in RPMI-1640 medium (name of medium used for the growth of Plasmodium that also contain RBC) present in bottle and were incubated to grow causative agent of the disease under laboratory conditions. The sample from these bottles were observed under microscope for presence of Plasmodium after 72 hours of incubation.

Furthermore, the surface water from stagnant regions of reservoir was collected in a container and was assayed for presence of larvae of mosquitoes with help of magnifying glass.

Laboratory Safety Procedure

All the containers having biological material was heated at 1210 C at 15 psi for 15 minutes to kill every type of living cells before discarding them. The chemical agents were discarded as described in their respective safety manuals

Result

Microscopy results show that the Red Blood Cell of diseased people were found to be infected with Plasmodium.

The causative agent of the disease was successfully found to grow in the medium that supports the growth of Plasmodium (Figure 3) and that was confirmed by microscopy e.g Plasmodium were found in the sample from medium under microscope and slides observed were found similar as shown in Figure 1 and Figure 2

Plasmodium

Figure 7 The blood sample after mixing with other reagent (as shown with white arrow indicating towards a tube) is then inoculated into the bottle ( as shown in picture) ( source Nature Protocols courtesy to Nature Publishing Group)

The surface water samples that was taken from the stagnant water collected near water reservoir were found of have mosquitoes larvae in large number as is shown in Figure 4.

Conclusion

The results that includes, presence of large number of mosquitoes larvae near the site of outbreak, Plasmodium was found in the infected blood of diseased people, that was successfully grown in the medium and conditions specific confirm that the outbreak is of malaria that was caused by Plasmodium.

Skill development to solve a Biological Problem

Report

A report comprising the incidences of malaria during the years 2002, 2003 and 2004, in three different cities was published in a newspaper. The patient were treated by using drug A and attempt to kill mosquitoes were made by spraying B into environment and adding in water collection. Out of these report related to three cities is given below.

In Karachi 530 malarial cases were reported in year of 2002, 534760 in 2003 and 12345668 in 2004 respectively. The mortality reported in these years were 98 in 2002, 120001 in 2003 and 5408889 in 2004.The rain fall recorded in Karachi for year 2002 is 50mm, 2003 is 100 mm and 10,000 mm in 2004.The drug resistance was found in 12 cases in 2002, 60009 cases in 2003 and 9900099 cases in 2004.

In 2002, 134 malarial cases were reported in Faisalabad where as in 2003 and 2004 the reported numbers were 1237 and 1379 respectively. The mortality reported in years 2002, 2003 and 2004 were 10, 99, 115 respectively. The annual rainfall reported in these years were 12 mm in 2002, 58 mm in 2003 and 89 mm in 2004.The antimalarial drug resistance was found to be in 2 cases in 2002 , 79 cases in 2003 and 91 cases in 2004.

In a similar study that was conducted in Gilgit during these three years, it was found that malaria affected 325 people in 2002, 135 people in 2003 and 350 people in 2004.There were 10 people reported to be died of malarial disease in 2002, 8 people in 2003 and 17 people in 2004. The annual rainfall reported in these years were 130, 120, 105 mm in 2002, 2003 and 2004 respectively. The resistance against anti malarial drug found in 2002, 2003 and 2004 were 9, 4, 9 respectively.

Source

( It is an imaginary situation given with data to help student develop research skills)

Research Methodology

Step 1 Identification of problem from published literature

After reading the above mentioned findings, management of heavy occurrence of malarial disease has been identified as a problem.

Step 2 literature search for generating the hypothesis taking malaria as a test case

Malaria is a very common infectious disease that is commonly associated with poverty. It is caused by protozoan parasites Plasmodium species that is transferred to human being blood circulation system by the vector Anopheles mosquito’s bite(1). Literature show that malarial outbreaks can be related with rainfall in that area (2). Malaria is more common in urban area than in cities. However in Africa it is present in both rural and urban areas (3,4)No literature is available about the relation of malarial incidence with location of the place with height above sea level. The occurrence of malarial outbreak can be related with presence of stagnant water that can support the mosquito survival in populations. Heavy use of anti-malarial drugs and mosquito cidal sprays is reported to produce resistance in the protozoa against commonly used drugs (5).

1) Cox F (2002). History of Human parasitology. Clin Microbiol Rev 15 (4): 595-612.

2) Grover-Kopec E, Kawano M, Klaver R, Blumenthal B, Ceccato P, Connor S. 2005 An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa. Malar J 4(1): 6.

3) Van Benthem B, Vanwambeke S, Khantikul N, Burghoorn-Maas C, Panart K, Oskam L, Lambin E, Somboon P 2005.Spatial patterns of and risk factors for seropositivity for dengue infection Am J Trop Med Hyg 72 (2): 201-8.

4) Keiser J, Utzinger J, Caldas de Castro M, Smith T, Tanner M, Singer B 2004. Urbanization in sub-saharan Africa and implication for malaria control. Am J Trop Med Hyg 71 (2 Suppl): 118-27.

5)Rieckmann, K.H.2006 The chequered history of malaria control: are new and better tools the ultimate answer? Annals of Tropical Medicine and Parasitology 100(8) 647-662

6) en.wikipedia.org/wiki/Malaria

(The Scientific literature is presented with citation of references as is shown in above paragraph)

Step 3 Deduction of hypothesis with help of published literature

After reading the reported literature as mentioned above following hypothesis can be deduced.

  • Incidence of malaria is dependent on amount of rainfall probably through collection of stagnant water

Step 4 Experimental design

The data for the parameters analyzed was collected by using standard methods e.g. microscopy of infected blood samples at different hospitals and was published in a newspaper as a scientific report.

Step 5 Presentation of Raw data

The above mentioned data is presented below in tabular form.

Karachi
Year Malarial cases reported Mortality Rainfall (mm) No of resistant cases to anti-malarial drug A
2002 530 98 50 12
2003 534760 120001 100 60009
2004 12345668 5408889 10000 9900099
Average/year 4293652.667 1842996 3383.33 3320040
Faisalabad
Year Malarial cases reported Mortality Rainfall (mm) No of resistant cases to antimalarial drug A
2002 134 10 12 2
2003 1237 99 58 79
2004 1379 115 89 91
Average/year 916.666 74.6666 53 57.333
Gilgit
Year Malarial cases reported Mortality Rainfall (mm) No of resistant cases to antimalarial drug A
2002 325 10 130 9
2003 135 8 120 4
2004 350 17 11000 9
Average/year 270 11.6666 3750 7.333

Data Analysis

(a) Data was analyzed by using different mathematical tools.

Karachi 8 m above sea level

Year Log10 (Malarial cases reported) %Mortality Log10(%Mortality)
2002 2.72427 18.4 1.264
2003 5.7281 22.44 1.351
2004 7.09151 43.81 1.6415
Average/year 5.1812 28.247 1.4188

Faisalabad 300m above sea level

Year Log10 (Malarial cases reported) %Mortality Log10(%Mortality)
2002 2.12 7.46 0.8723
2003 3.092 8.0 0.903
2004 3.139 8.33 0.9206
Average /year 2.7836 7.93 0.8986

Gilgit 1500m above sea level

Year Log10(Malarial cases reported) %Mortality Log10(%Mortality)
2002 2.51 3.076 0.487986
2003 2.130 5.92 0.77
2004 2.54 4.8 0.68
Average/year 2.3933 4.59866 0.64

Interpretation of data

The results were analyzed to estimate the dependency of incidences of malarial disease with amount of rainfall.

The frequency of incidence of malarial cases seems to be dependent on amount of rainfall at sea-level places but this dependency is diminished at places at higher altitude from sea level.

Theory

The data supports the hypothesis that incidences of malarial cases are dependent on amount of rainfall at sea-level places and it is accepted as theory.

Publication

A scientific paper is written on present understanding accommodating the latest finding obtained and sent to scientific Journals.

Hypothesis Incidence of malaria is dependent on amount of rainfall probably through collection of stagnant water

Data analysis

Theory incidences of malarial cases is dependent on amount of rainfall at sea-level places.

Result s interpretation

How to draw a best fitted line for a given data

A data for a biological experiment, having n different independent variables such x1 x2 x3…..xn and its dependent variables y 1 y2 y3………..yn is needed to be plotted with best fit line or regression line. Trendline, best fitted line,

slope

Regression line least square line are the different terms used for the same thing.

The general formula is y=mx+b------equation 1Y axis where m=slope, b=y-intercept

Intercept is a point where a line passes an X axis. Y-intercept represents the value of Yb=y-intercept at which a line crosses it ( x=0).

Slope= m=n(Sxy)-( Sx)( Sy) ---------- equation 2n(Sx2)- (Sx)2Y-Intercept =b=Sy- m(Sx)-----equation 3n

Where Sxy=x1y1+x2y2+x3y3---xnyn

Sx= x1+x2+x3………xn

Sy= y1+y2+y3……..yn

r = +1

Sx2= x12+x22+x32……..xn2

The values for Slope=m and y-Intercept=b are calculated after putting the values for individual variables in equation 2 and 3 respectively

The calculated values for Slope=m and Intercept=b are substituted in equation 1.

A best fitted line graph is drawn by plotting the values calculated for y by using equation 1 for a given value of x after substituting the calculated value of slope=m and y-intercept.

The Co-efficient of correlation is a quantitative measure to represent how well the data points got accommodated on the trend line.The value of r ranges from –1 to +1. It is-1 for a negative slope line and +1 for a r = -1 positive slope line. The value of r when is closer to zero represents poorer fitted data whereas better fitted data is shown by a value closer to either –1 or +1.

Co-efficient of co-relation = n(Sxy)-( Sx)( Sy) [n(Sx2) -(Sx)2 ]0.5 [n(Sy2) -(Sy)2 ]0.5

Exercise

Answer following question

  • How is superstition different than hypothesis?
  • Each time the observation is recorded is usually slightly different than earlier observation recorded in the same conditions. In such a situation how does it expected that error is decreased with increase in number of recording the observation?
  • Can any one get an error free observation? Justify your answer with scientific reasons.
  • A student was given a task to measure the length of two piece of clothes (A) was of length 150.75 cm whereas B was with 3.25cm with either of two tapes one having 1 cm as a least count with a capacity to measure a length of 50 cm whereas other with a least count of 0.2 cm for a measurement of length of 10.50.

Which of these two measuring tape is more suitable for measurement of piece A and which one more is suitable for measurement of B with least error in both the cases. Please explain your answer with scientific reasons.

  • Why does most of biological data is plotted on the graph on log10 scale?

Please encircle the most appropriate answer.

1) The scientific experiments are done………. times to ensure that data and findings of the experiment are reproducible.

a) one time b) more than one (c) none of them

2) If the results of experiment in the three set of experiments e.g. test, positive control and negative control are same then it means---------------------------

a) There has been some technical problem due to which experiment has not worked

b) Experiment worked very well

c) None of them

3) A positive control experiment is set to gives a result same as------------------- in a given set of conditions

a) positive b) as negative result c) test

4) A Negative control experiment is set to gives a result same as------------------- in a given set of conditions

a) positive b) as negative result c) test

5) The results of an experiment that was done at one set obtained at one set of conditions are expected to be------------- when the same experiment is done at different set of conditions.

a) same b) different c) identical

Please match the complementary words given in two column

Source of error in data collection (a) Random sampling

(b)Several Replicate of experiments are done at a given conditions

Method to minimize the error (d) Eating of Fish with milk causes vitiligo

e)Light is source of energy

Superstition (f)Handing of sample

(g)Variation in conditions of experiments

(h)Recording of data

Theory (i) Biasness in sampling

(j)Air contains oxygen

Theory discarded(k) Sun rotates round the earth

(l)Light comes out of eyes

Thinking Exercise for implication of scientific methodology

a) Please imply the scientific methodology to establish the status( accepted, rejected, modified) on at least two hypothesis related to above given problem deduced after referring the scientific literature cited. Student and teachers are encouraged to use excel.

Solution

Part 1.

Since the problem is same so step 1 and step 2 are identical as done earlier to generate a hypothesis listed below

Step 3 Deduction of hypothesis in light of literature

Another hypothesis that was built up on basis of scientific literature given is listed below

  • Incidence of malaria is decreases with increase in height above sea level

b) Data analysis to find out the effect of altitude above sea level on incidences of malarial cases.

Karachi 8 m above sea level

Year Log10 (Malarial cases reported) %Mortality Log10(%Mortalit-y) Log10 (Rain fall)( mm)
2002 2.72427 18.4 1.264 1.698
2003 5.7281 22.44 1.351 2
2004 7.09151 43.81 1.6415 4
Average/year 5.1812 28.216 1.4188 2.566

Faisalabad 300m above sea level

Year Log10 (Malarial incidence reported) %Mortality Log10(%Mortality) Log10 (Rain fall) (mm)
2002 2.12 7.46 0.8723 1.079
2003 3.092 8.0 0.903 1.7634
2004 3.139 8.33 0.9206 1.949
Average /year 2.7836 7.93 0.8986 1.5973

Gilgit 1500m above sea level

Year Log10(Malarial cases reported) %Mortality Log(%Mortality) Log10 (Rain fall)(mm)
2002 2.51 3.076 0.487986 2.11
2003 2.130 5.92 0.772321 2.07
2004 2.54 4.8 0.6812 4.0413
Average/year 2.3933 4.59866 0.6471 2.74043

The average malarial incidences reported during three years were plotted against the altitude of different cities on log scale.

Interpretation of data

The results were analyzed to estimate the dependency of incidences of malarial disease on altitude of places

The incidences of malarial cases seems to be independent on altitude of places as there is considerable decrease in incidences observed in Faisalabad which is just 292m above sea level in comparison to Karachi whereas there is not much difference in number of incidences of malaria reported in Faisalabad and Gilgit whereas Gilgit is 1200m higher above the sea level as compare to Faisalabad.

Hypothesis

On the basis of above mentioned reason the hypothesis that incidences of malarial cases decreases with increases in altitude has been discarded.

Theory

On the basis of studies it is established that the incidences of malarial cases does not decrease with increase in altitude.

Publication

The scientific finding that the incidences of malarial cases does not decrease with increase in altitude.

Incidence of malaria cases does not decrease with increase in altitude is accepted

Part 2.

Since it is the continuation of earlier problem so step 1 and step 2 will remain the same.

Step 3 Deduction of hypothesis in light of literature

Another hypothesis deduced on basis of finding reported is

  • Resistance to malarial drug decreases with increase in height above sea level e.g. related occurrence with malarial incidences

c) Data analysis to find out the effect of altitude above sea level on incidences of resistance against malarial drug A cases.

Karachi 8 m above sea level

Year Log10(Malarial incidence) %Mortality Log10(%Mortality) Log10 (Rain fall)(mm) % Resistant cases to anti malarial drug A Log 10(% Resistance against anti malarial drug A)
2002 2.72427 18.4 1.266 1.698 2.26 0.3541
2003 5.7281 22.44 1.351 2 11.22 1.0499
2004 7.09151 43.81 1.6415 4 80.19 1.904
Average/year 5.1812 28.216 1.4198 3.529345 77.32437 1.888316

Faisalabad 300m above sea level

Year Log 10 (Malarial cases %Mortality Log 10 (%Mortality) Log10 (Rain fall)( mm) % Resistant cases to anti malarial drug A Log10(% Resistant cases against anti malarial drug A)
2002 2.12 7.46 0.8723 1.079 1.49 0.173
2003 3.092 8.0 0.903 1.7634 6.38 0.8048
2004 3.139 8.33 0.9206 1.949 6.59 0.8188
Average /year 2.7836 7.93 0.8986 1.5971 4.82 0.599

Gilgit 1500m above sea level

Year Log 10 (Malarial cases reported) %Mortality Log10(%Mortality) Log 10 (Rain fall)(mm) % Resistant cases to anti malarial drug A Log10(% Resistant cases to anti malarial drug A)
2002 2.51 3.076 0.487986 2.11 2.76 0.4423
2003 2.130 5.92 0.772321 2.07 2.9 0.472
2004 2.54 4.8 0.6812 4.0413 2.57 0.410
Average/year 2.3933 4.59866 0.6471 2.74 2.76 0.44142

The average resistance against malarial drug A incidences reported during three years were plotted against the altitude of different cities on log 10 scale.

Interpretation of data

The results were analyzed to estimate the dependency of resistance against malarial drug A on altitude of places

The data show that prevalence of resistance against malarial drug A is found to be independent of altitude of places

Hypothesis

On the basis of above mentioned reason the hypothesis that prevalence of resistance against malarial drug A is found to be independent of altitude of places has been discarded

Publication

A scientific paper is written on present understanding accommodating the latest finding obtained and sent to scientific Journals.

Resistance to malarial drug decreases with increase in height above sea level

Incidence of malaria is dependent on amount of rainfall probably through collection of stagnant water

Incidence of malaria is decreases with increase in height above sea level

Literature

Experiment design

Theory incidence of malaria decreases with increase in height above sea level is accepted

Hypothesis e.g incidence of malaria is decreases with increase in height above sea level is accepted

Data collection

Modified Hypothesis e.g. incidences of malarial cases seems independent of amount of rainfall at higher attitude places.

Data analysis

Results interpretation

Hypothesis discarded e.g. resistance against malarial disease decreases with increase in altitude of place

Modified Hypothesis e.g. incidences of malarial cases seems independent of amount of rainfall at higher attitude places but is dependent at sea level.

Hypothesis e.g. incidences of malarial disease decreases with altitude of place is modified

Theory incidences of malarial cases is dependent on amount of rainfall at sea-level places.