Scientific Method

RESEARCH

searching for a theory (a scientifically acceptable general principle offered to explain observed facts). For testing a theory, or for solving a problem.

a SYSTEMATIC, CONTROLLED, EMPIRICAL, and CRITICAL investigation of hypothetical propositions about the presumed relations among natural phenomena (Kerlinger, 1973) SYSTEMATIC – follows steps or stages that begin with identification of the problem, relating of this problem with existing theories, collection of data, analysis, interpretation of these data, drawing of conclusions, and integration of these conclusions into the stream of knowledge.

CONTROLLED – is so planned every step of the way that fancy and guess work do not set in. The problem is defined thoroughly, variables identified and selected, instruments carefully selected or constructed, conclusions drawn only from the data yielded, and recommendations based on the findings and conclusions.

EMPIRICAL DATA – will form the bases for conclusions. Everything is so controlled that any observer of the investigation will develop full confidence in the results.

CRITICAL ANALYSIS – is done by a panel of judges that passes judgment on the entire research. an ORGANIZED and SYSTEMATIC way of FINDING ANSWERS to QUESTIONS ORGANIZED – involves a structure or method in going about doing research. It is planned procedure, not a spontaneous one. It is focused and limited to a specific scope. SYSTEMATIC – follows a definite set of procedures and steps. There are certain things in the research process which are always done in order to get the most accurate results.

FINDING ANSWERS – is the end of all research. Whether it is the answer to a hypothesis or even a simple question, research is successful when we find answers.

Sometimes the answer is no, but it is still an answer.

QUESTIONS – are central to research. If there is no question, then the answer is of no use. Research is focused on relevant, useful, and important questions. Without a question, research has no focus, drive, or purpose. * sometimes called a term paper or library paper, an ordinary critical essay or the more daunting thesis (an essay embodying results of original research especially one written for an academic degree or dissertation (an extended usually written treatment of a subject especially one submitted for a doctorate) reports the writer’s research findings.

involves “searching again” through what others have written about the subject.

is primarily characterized by its use of data gathered from a wide range of sources to clarify, analyze, expound on, discover, discuss, and debate an idea.

entails understanding a scholarly endeavor and acquainting yourself with the variety of materials at your disposal (e. g. , the library, various institutions, field interviews, questionnaires, the internet, email, and the like) to support your claims.

TWO APPROACHES

• (1) a summary of information from many resources

If the paper summarizes research, it reports the reading from a single source or, more likely, from many sources.

• (2) an evaluation of research information If the paper evaluates the research information, it considers why or how and is frequently either a comparison paper or a cause-effect paper. The evaluation paper requires the use of numerous sources and assumes the writer’s ability to show originality and imagination.

CHARACTERISTICS

An effective research paper fulfills these requirements:

• indicates careful, comprehensive reading and understanding of the topic establishes, in its introduction, a thesis to be developed in the course of the paper
• follows a clear organization
• employs the principles of good composition
• includes direct quotations, paraphrases, or precis that supports the thesis
• includes documentation in the form of parenthetical notes, endnotes, or footnotes
• includes a list of works cited
• exhibits careful, thorough documentation o sources of ideas
• follows a carefully prescribed format
• is almost always typed or, if prepared on a computer, printed on a letter-quality printer

REMEMBER!

A research paper uses documentation analyzes, discusses, and debates ideas acquaints you with a cross section of materials engages you in critical, not creative, reading and writing|

A research paper is not a piece of expository writing personal essay reflection paper* review of academic literature mere reporting of facts and/or opinions |

How to Write Analytical or Argumentative Research Papers By Joe Robertson Research papers can be easily differentiated from personal essays on the basis of the extensive research that is executed before the writing of such papers.

Research papers thus act as that creative output in which the writers’ personal thoughts and opinions are merged with theories from already established sources. However, the technique used in the presentation of the paper may make it fall under two broad categories:

1.  Analytical,
2. Argumentative, in fact the strategy used by the writer to compose his paper will eventually determine the aim and purpose of the paper. A detailed discussion of these two methods will clarify the concepts presented above:

1. Analytical Papers

In an analytical research paper, the aim is to attain a thorough expertise of the concept that is being presented so that it can be broken down and represented from the writers’ point of view. In this form of the research paper, an individual approaches the research question without any pre-conceived notions and ideas about the subject at hand. Thereafter a careful survey of the opinions and views is undertaken. Ultimately when familiarity with the topic is achieved; a person is able to restructure and relocate the concepts that underlie the basic topic in his paper; the very essence of an analytical paper; critical contemplation and valuation of the question at hand is necessary for an analytical paper.

2. Argumentative Papers This type of a paper may also be termed a persuasive paper.

Aside from critical thinking which is essential for the production of a quality paper, another familiar concept that dominates academic circles is the concept of an argument. The basic difference from the former kind that qualifies the persuasive kind is that the paper takes a conscious stance and argues in favor of one of the arguments with cogent facts and points presented in its favor.

The aim is to mould the reader’s mind in favor of one possible answer to the research question backed by reliable data and arguments. Both approaches require logical thinking and smart evaluation alongside comprehensive research of the available sources. However the difference is created through the process of writing, analytical papers provide a more balanced approach where all views pertaining to the question are presented whereas argumentative papers debate in favor of one logical solution above the others.

Chromatography is a separation technique in which the mixture to be separated is dissolved in a solvent and the resulting solution, often called the mobile phase, is then passed through or over another material, the stationary phase. The separation of the original mixture depends on how strongly each component is attracted to the stationary phase. Substances that are attracted strongly to the stationary phase will be retarded and not move alone with the mobile phase. Weakly attracted substances will move more rapidly with the mobile phase.
Liquid chromatography is an analytical technique that is useful for separating ions or molecules that are dissolved in a liquid phase. If the sample solution is in contact with a second solid or liquid phase, the different solutes will interact with the other phase to differing degrees due to differences in adsorption, ionic strength, polarity or size. These differences allow the mixture components to be separated from each other by using these differences to determine the transit time of the solutes through a column.

Simple liquid chromatography consists of a column with a fritted bottom that holds a stationary phase in equilibrium with a solvent. Typical stationary phases (and their interactions with solutes) are: solids (adsorption), ionic groups on a resin (ion-exchange), liquids on an inert solid support (partitioning), and porous inert particles (size exclusion). The mixture to be separated is loaded onto the top of the column followed by more solvent. The different components in the sample mixture pass through the column at different rates due to differences in their partition behavior between the mobile phase and the stationary phase. The compounds are separated by collecting aliquots of the column effluent as a function of time.

Conventional Liquid Chromatography is most commonly used in preparative scale work to purify and isolate some components of a mixture. It s also used in ultra trace separations where small disposable columns are used once and then discarded. Analytical separations of solutions for detection or quantification typically use more sophisticated high-pressure liquid chromatography instruments.

In liquid chromatography, the separator is called the column and consists in most cases of a tube filled with porous material called the stationary phase. A liquid, called the mobile phase, flows through the tube between the particles of stationary phase material. A liquid sample is taken from a mixture to be analyzed and introduced to a part of the system that is at elevated pressure. The sample is then transported to a separator by the flow in the system.

After the column the separated compounds enter the detector, which measures a physical or chemical property of each, now relatively pure, compound and creates a proportional electronic signal. By calibrating with a standard mixture of known compounds, the nature of the compound in the mixture can be elucidated. The quantity of the relevant compounds in the mixture can be calculated by integration of the signal.

The components in the sample become distributed differently between the mobile and stationary phases because they have different interactions (Physical and chemical) with each phase. You can select the nature and strength of the interactions by your choice of phases. The components move with different speeds through the column, depending on their affinity for the different phases, and so they elute from the column at different times: the retention times. By changing the composition of the mobile phase during the elution, its possible to analyze a wider variety of compounds in a given time than would be possible under constant conditions.

Chromatography is used in chemistry and biochemistry research analyzing complex mixtures, purifying chemical compounds, developing processes for synthesizing chemical compounds, isolating natural products, or predicting physical properties. It is also used in quality control to ensure the purity of raw materials, to control and improve process yields, to quantify assays of final products, or to evaluate product stability and monitor degradation.

In addition, it is used for analyzing air and water pollutants, for monitoring materials that may jeopardize occupational safety or health, and for monitoring pesticide levels in the environment.

Name: Ornella Hayles

ID Number: 816008392

Tutor: Sheldon Pilgrim

Session: Tuesdays 2-3pm3700030000

Name: Ornella Hayles

ID Number: 816008392

Tutor: Sheldon Pilgrim

Session: Tuesdays 2-3pm44000341947525002514604000070000455003536315690006939915370000455003536315350003520440Sci, Med & TechReflective Journal3600028000Sci, Med & TechReflective JournalSession

Date: 22/01/18

Session No: 1Session

Title: Introduction to Science & TechnologyIn the first session, the lecturer did an introduction of Science & Technology and an overall view of the course outline.

After the introduction, I got a perspective of what Science & Technology was. At first, I thought the course was going to be essentially, about science since it was mainly being mentioned, but I was wrong. The lecturer took her time to clearly distinguished between science and technology and its importance to society or the world in general.

She ensured that we all understood what she was lecturing by engaging us to respond to her questions, it appeared that she really wanted us to understand what was being taught so she went over what she said twice, two different ways which I appreciated. It helped me a lot to understand and grasp the concepts effectively. The use of visual cues seemed to work well with the class as it captured our attention and made us receptive.

Through research I’ve come to realise scientists all have different perceptions of the word science. “Science does not purvey absolute truth, science is a mechanism. It’s a way of trying to improve your knowledge of nature, it’s a system for testing your thoughts against the universe and seeing whether they match” Isaac Asimov 1988. Science is important because it can solve some of our problems such as global warming and world hunger.

I believe that science is still evolving and there’s trial and error with science which means we learn new information and we advance as humans but, it can all take years for such research or technology to be developed. “Technology can be thought of as the application of scientific knowledge for practical purposes”. I’ve always considered technology and science to be separate now, through critical analysis I know that science and technology are closely associated with each other. Which means technology is a part of science, it has been in existence since the prehistoric human culture (stone age).

In my opinion technology is everywhere although we might not realise it our note books are a form of technology whereby we use our knowledge to document for practical purposes like computers. Overall for my first time doing this course it was challenging but, I’ve come to appreciate science and technology more, subsequently I can’t imagine having to sleep without a roof above my head or having to cook using sticks and rocks.

Through this experience I’ve got a spark of curiosity that makes me more enthusiastic to know more about science and technology. Scientists have reached so far in the field of technology whereby people can afford to do scientific research and make scientific break throughs from their own houses. The society we all know and accustomed to would be non-existent if it wasn’t for the advancement in technology. We’re so dependent on science and technology without realising it.

23 MORE WORDS Session

Date: 29/01/18

Session No: 2

Session Title: Scientific Methods & The Nature of ScienceFor the second session, the lecturer started class with a recap of science. “All of science is uncertain and subject to revision.

The glory of science is to imagine more than we could prove” Freeman Dyson. To follow up with the course outline, she started the second topic. To my understanding science is like a puzzle, to see the full image you need to put the pieces together. This can be distinguished through the scientific process. “The scientific method is a series of steps followed by scientific investigators to answer specific questions about the natural world” Regina Bailey 2017.

What surprised me the most about this process is the fifth step experimentation, which is the most important step in this process. This is so because it can cause major breakthroughs in the world of science or a reversal. Through science we can make technological advancements and end some of the world major problems. “Fields of engineering are closely related to applied science.

Applied science is important for technology development” Wikipedia 2018. It is understood that through scientific knowledge scientists have been able to provide services for the wellbeing of humans. Through many experiments people have been saying technology is good because it helps people medically and physically, it connects us as human beings and gives us entertainment. On the plus side technology is causing us to become less social.

With this experiment, it shows how technology is applicated through private and public knowledge orientation. “Scientific objectivity is a characteristic of scientific claims, methods and results. It expresses the idea that the claims, methods and results of science are not, or should not be influenced by particular perspective value commitments, community bias or personal interests, to name a few relevant factors. Objectivity is often considered as an ideal for scientific inquiry, as a good reason for valuing scientific knowledge, and as the basis of the authority of science in society” Stanford Encyclopedia of philosophy 2014.

To my knowledge there are three methods that scientists use inductive approach, deductive approach and hypothetico -deductive approach. The inductive approach and deductive approach are opposite, where the inductive approach is based off fact then there’s a conclusion whilst deductive approach is a valid type of reasoning. Induction starts with observation, then a pattern, tentative, hypothesis and finally a theory. “In induction inference, we go from the specific to the general.

We make many observations, discern a pattern, make a generalisation, and infer an explanation or a theory” Wassertheil Smoller 2017. Deduction has four stages theory, hypothesis, observation and confirmation. “Deductive reasoning is a logical process in which a conclusion is based on the concordance of multiple premises that are generally assumed to be true” Tech Target. Lastly hypothetico- deductive method which is seen as the only true method.

What I understand is that the only way to truly test this is through experimentation which you either accept or reject the hypothesis and finally have an example. This week’s session journal was manageable enough for me.

Session Date: 05/02/18

Session No: 3

Session Title: Scientific Methods, Theories and ModelsIn this week’s session, the lecturer started with the scientific method, then followed with theories and lastly models. Science is empirical, which means that it is based on observation over theory or logic. In class, we briefly discussed the history of scientific methods. It is understood that Plato 429-347 BC did not believe in empiricism but reasoning.

“All knowledge could be obtained through pure reasoning (inductive), no need to actually go out and measure anything,” Plato 427-347 BC. “Contributions have been more influential, particularly when it comes to science and logical reasoning (deductive)” Aristotle 384-322 BC. Aristotle believed in empiricism.

Personally, I agree with both Plato and Aristotle, for me science can be based on observation and reasoning also through empiricism where there’s a logical way to test such observations. A major point in this week’s session was observation. Observation is knowledge or data we acquire through experimentation. An example of observation from what I understand is the writing up of labs (biology or chemistry) for labs you have to observe then write what you understand.

Through my research in observation I came across this question which intrigued me “Is creative concentration contagious?” Lynda Barry 2011. This brings me into another key example in topic three. When a baby sees his mother drinking in a cup, he tries to do the same with his cup, he holds it up and tries to take a sip from his cup. The baby first observes, then experiments.

There are two types of observations, qualitative observations which uses your senses to observe the results and quantitative observations are made with instruments such as ruler, cylinders and thermometers which are tangible. These results are measurable. They could be used together or separately to measure data. Quantitative observation only gives statistical analysis after all the data has been gathered. What I understand from scientific theory is that it’s a repetition of a process of the natural to ensure the accuracy or legitimacy using a form of observation and experiment.

An example of this would be no new evidence would show water is wet, or that you can see without your glasses. Experimentation is the process of performing a scientific procedure, especially in a laboratory, to determine something” Oxford dictionaries 2018. From the information, I gathered from the session so far is that observation and experimentation are relate and there are two types of observation. “A statement based on repeated experimental observations that describes some aspects of the universe is called a scientific law’ Wikipedia 2017. A scientific law doesn’t explain the why or what of this observed phenomenon.

The explanation of this phenomenon is the scientific theory, this is why or how they are related. “In science, laws are a starting place” Peter Coppinger 2017. The description of such phenomenon is called a model. These models can be physical, conceptual or mathematical it is often used in scientific theories.

Objective There are many websites and newspapers giving predictions in this direction, but there is no tool which can give mathematical analysis about the races. For my Data Mining Project I will use a database collected from www. Greyhound-data. Com, then I will use this data in Reprimanded to generate a random race sample and finally I will predict the winner of the race using the same tool. Database The database collected is comprised of 100 examples with 11 dimensions: 1. Place – which represents the national rank 2.

Name – II/II represents the land of standing/land of 3. Land of Birth 4. Land of Standing 5. Year of birth 6. Sex – male or female 7. Sire – father’s name 8. Dam – mother’s name (the last two dimensions are considered important in ambling) 9. Races – the number of races for 2014 10. Points – how many points each dog heave accumulated in 2014 11. Bag Didst – the average distance of races. All the details are based on 2014 statistics collected from the website up mentioned. On top of these dimensions I manually added three more: 1. Weight – in Keg 2. Owner 3.

Color The last three heave missing data, which make the dataset noisy but I will try to find the best way to recover the missing data. After importing the dataset in Dynamiting from an Excel file, first I analyses the data, then I separated clean data from dirty ATA (no_missing_attributes function). As a result, only 29 items were perfect data, while 71 had missing values (noisy). As we can see in the picture the missing values are highlighted in red. Removing Noise First method used to remove the noise is using the “average” function provided by Reprimanded.

A graphical representation of the design of this method can be seen in the next picture. With this method I replaced “all” missing values with the “average”. Generate a Sample Next step is to generate a sample of six items because this is the number of dogs competing in a race. This sample is random generated and the result is: As we can see highlighted in red the national rank is close, which means that the race will be very tight and very hard to predict as well. In the last results I noticed that there is some data that I do not need to use for my final analysis and I decided to remove it.

To do this I used “Remove Useless Attributes” as shown in the next picture: Then the results will look like this: Now is more simple to read data, with only 12 dimensions left. Phase 3 – The Results In this part I will try to predict which of the six dogs will win the race. I will use two ethos, one is the “Aggregate” function and the other is “Attribute Generation”. First, I decided to remove some of the attributes as not all of them are actually needed for this operation.

To do this, I used “Select Attribute” function, as shown in the picture below. Six attributes will be enough for the next operation and final operation to find the winner. Next, I will use “Aggregate” operator and I will use the attribute “points” to generate the winner. After I add this operator in the design window, one click is needed to display its functions on the right hand sand. After I clicked on “Edit List”, a Indo opened, where I selected the attribute “Points” on the left and the “maximum” function on the left (next picture).

Now we can run the process to see the result: As we can see, based on “Points”, the possible winner is the number one dog on the list because he has the highest number of points. This result can be considered, as the points accumulated are the most important decisional factor when we want to check the “favorite” for a dog race. But because the points are not the only factor to consider, another method has to be found. Next, I will present another solution, which looks even more interesting. It involves weighting the more than one attribute and this is why this method looks better.

I removed “Aggregate” operator and I added another two instead: “Set Role” and “Generate Attribute”. I used Set Role attribute to generate a label (picture below – on the right), in this case I choose name. In the next picture is described the Generate Attribute operator. I clicked “Edit List” (number 1) on the right hand side and a new window opened. In this window, new attributes can be generated. At number 2 is defined the new attribute name which is “Winner” in my case, than at number 3 a formula is introduced. The formula weights three attributes “Weight”, “Races” and “Distance”.

Based on them, Reprimanded will calculate a score for each dog. The results are shown in the next picture In red is highlighted the winner, number one – Austrian Lisa, and in black is the new generated attribute – “Winner”, which shows the results for all the competitors. Conclusions This model can be used betting companies like Powdery for example to generate odds for example, but it can be used as well by people who have a passion for gambling. It can be also used to build a website which calculates the winners for future races and attract visitors this way.

Introduction

A regression model with one explanatory variable is called a Simple linear regression, that is it involves 2 points: single explanatory variable and the response variable which is the x and y, coordinates in a Cartesian plane and finds a linear function a non-vertical straight line that, as precisely as possible it explains the dependent variable values as a function of the independent variables.

The term simple refers to the fact that the response variable y is related to one predictor x. The regression model is given as Y=?0+?1 + ? and they are two parameters that are used estimate the slope of the line ?1 and the y- intercept of the line ?0. ? is the random error term.BackgroundRegression analysis is a vital statistical method for the analysis of medical data.

It makes it possible for the recognition and grouping of relationships among multiple factors. It also enables the recognition of prognostically relevant risk factors and the calculation of risk scores for individual prognostication, this was made possible by English scientist Sir Francis Galton (1822–1911), a cousin of Charles Darwin, made significant contributions to both genetics and psychology.

He is the one that came with regression and a pioneer in using statistics in a study of living organism. In his study the data sets that he considered consisted was the heights of fathers and first sons. He wanted to find out whether he can predict the height of a son based on the father height. Looking at the scatterplots of these heights, Galton saw that the was relationship which was linear and increasing.

After fitting a line to these data using the statistical techniques, he observed that for fathers whose heights were taller than the average, the regression line predicted that taller fathers tended to have shorter sons and shorter fathers tended to have taller sons.PurposesSimple linear regression could be for example be purposefully when we Consider a relationship between weight Y (in kilograms) and height X(in centimeters), where the mean weight at a given height is ?(X) = 2X/4 – 45 for X > 100.

Because of biological variability, the weight will vary for example, it might be normally distributed with a fixed ? = 4. The difference between an observed weight and mean weight at a given height is referred to as the error for that weight. To discover the relationship which is linear, we could take the weight of three individuals at each height and apply linear regression to model the mean weight as a function of height using a straight line, ?(X) = ?0 + ?1X .

The most popular way to estimate the parameters, intercept ?0 and slope ?1 is the least squares estimator, which is derived by differentiating the regression with respect to ?0 and ?1 and solving, Let (xi , y i ) be the Ith pair of X and Y values. The least squares estimator, estimates ?0 and ?1 by minimizing the residual sum of squared errors, SSE = ?(y i – ? i)2, where y i are the observed value and ?i = b0 + b1xi are the estimated regression line points and are called the fitted, predicted or “hat” values.

The estimates are given by b0 =¯y – b1 ¯x and b1 = SSXX / SSYY, and where ¯Xand ¯Y are the means of samples X and Y, SSXX and SSYY being their standard deviation values and r = r(X,Y) being their Pearson correlation coefficient. It is also referred to as Pearson’s r, the Pearson product-moment correlation coefficient, is a measure of the linear between two variables X and Y Where X is the independent variable and Y being the Dependant variable as stated above.

The Pearson correlation coefficient, r can take a range of values from -1 to +1. A value of 0 suggests that there is no association between the two variables X and Y. A value greater than 0 indicates a positive association that is, as the value of one variable increases, so does the value of the other variable.

Before using simple linear regression analysis it is always vital to follow these few steps: Choose an independent variable that is likely to cause the change in the dependent variable Be certain that the past amounts for the independent variable occur in the exact same period as the amount of the dependent variable.

Plot the observations on a graph using the y-axis for the dependant variable and the x-axis for the independent variable review the plotted observations for a linear pattern and for any outliers keep in mind that there can be correlation without cause and effect.ImportancesSimple linear regression is considered to be extensively useful in many practical applications and methodologies.

Simple linear regression functions by assuming that the variables x and y have a relationship which is linear within the given set of data. As assumptions are and results are interpreted, persons handling the analysing role in a such data will have to be more critical because it has been studied before that there are some variables which inhibit marginal changes to occur while others will not consider being held at a fixed point.

Although the concept of linear regression is one complex subject, it still remains to be one of the most vital statistical approaches being used till date. Simple linear regression is important because it has be wildly being used in many biological, behavioural , environmental as well as social sciences.

Because of its ability to describe possible relationships between identified variables independent and dependent , it has assisted the fields of epidemiology, finance, economics and trend line in describing significant data that proves to be of essence in the identified fields. More so, simple linear regression is important because it provides an idea of what needs to be anticipated, more specially in controlling and regulating functions involved on some disciplines.

Despite the complexity of simple linear aggression, it has proven to be adequately useful in many daily applications of life.

In this section, I will discuss the methodological approach that I intend to use to conduct this study. Then I talked about the sampling strategy and data collection tools. I have also talked about the data analysis methods. The methodological approach to conduct this study will be mix-method. Johnson and Onwuegbuzie (2004) defines mixed methods research as ‘the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts or language into a single study’ (p. 17).

The goal of mixed methods research is not to replace either qualitative or quantitative approaches but rather to draw from the strengths and minimize the weaknesses of both in single research studies and across studies (Hoshmand 2003). I want to use quantitative approach to analysis the figures that I intend to get from the documents review or data bases to see the change of the trends in the cotton industry before and after the introduction of fair-trade. On the other hand, I want to use qualitative approach to see ‘why’ and ‘how’ these changes take place.

Bryman (2008) maintains that interviews are probably the most widely employed method in qualitative research and give deep insights into how respondents’ view the world. Interviews used in qualitative research are termed as ‘qualitative interviews’ that include unstructured and semi-structured interviews (ibid: 436). The unstructured interviews tends to be very similar in character to a conversation (Burgess 1984), in which the researcher uses at most an aide-memoir as a brief set of prompts to him/herself to deal with a certain range of topics. These interviews are not suitable for current study as they are loose in nature where researchers have little control over interview procedure. The total unstructured nature of these interviews may lead discussion to different directions and distract the researcher.

However, the relatively unstructured nature of the semi-structured interviews and its capacity to provide insights into how research participants view the world is considered vital for this study. There are two main reasons of using semi-structure interviews. Firstly they provide a guide (interview guide) to the researcher to keep the interview procedure on the tract. This helps to get the maximum relevant information in the limited time. Secondly, the semi-structured interviews give the respondent a freedom to respond in a way that may raise new questions. This helps to develop the interview guide for the next interviews to get more relevant information.

A sample is a finite part of a statistical population whose properties are studied to gain information about the whole (Webster 2003). When dealing with people, it can be defined as a set of respondents (people) selected from a larger population for the purpose of a survey. Probability sampling, or random sampling, is a sampling technique in which the probability of getting any particular sample may be calculated. This kind of sampling is mostly used in quantitative research. Qualitative research, on the other hand, uses small samples, and does not aim to generalise the findings. This kind of sampling is known as non-probability sampling. According to Yin (2009) the focus of qualitative research is not normally on representation, rather it is concerned with ‘how and why’ people interpret the world in certain ways.

In this study, I intend to sample only for qualitative interviews. Here, I propose the use of random sampling. The samples will be selected from people working in cotton industry, cotton producer and trader. I will used this instrument to analysis the statics of cotton business before and after the introduction of fair-trade in UK. And i will analysis the results using the graphs. The selected portions of interviews will then be transcribed for further analysis. The data then will be compressed, categorized, and organised into themes prior to interpretation and extracting meaning from them. As the interviews are semi-structured, the questions will be written so that certain themes will be followed. This will help to code and organize the data, by identifying and associating significant themes and patterns and relating them to the research questions.

There are many issues related to my study on which research can be done such as, consumer behaviour regarding fair trade, views of farmer and producer after working under fair trade mark. However due to the limitation of study i will only focus on the impacts of fair trade on cotton business in UK. In conclusion, my research and findings will help to understand the difference of Fair trade and non Fair Trade business and it will also help new researcher in the field of cotton to understand the change in cotton business after fair trade trading in UK.

It is common knowledge that human beings commit errors in judgment all the time. In areas of uncertainty, most of us go with our gut intuition, and in most cases this intuition turns out to be wrong. Much of this is derived from the fact that humans are poor statistical thinkers, and thus poor Bayesian thinkers. What is Bayesian thinking? Let us start with an illustrative example, called the Monty Hall problem — famously depicted in the Kevin Spacey movie “21.”

There are three doors, and behind each door is either a goat or a car. There will always be two doors with goats and one door with a car. The player first chooses a door without opening, and the game show host whose interests are opposed to the player, proceeds to open a different door. Since the host knows what is behind each door, he always opens a door with a goat. Now that the player is left with the initially chosen door and another closed door, the host offers an opportunity to switch to the other unopened door.

Should the player switch? The answer for an intuitive Bayesian, a purely statistical thinker, should be easy. Unfortunately, human beings are not intuitive Bayesians. In fact, most people answer that it doesn’t matter if the player switches or not, since the probability of winning a car is 50% between the two doors anyways.

They would be wrong. Now, before we examine the correct way to think about this problem, one might ask, so what? Why does it matter if humans are not intuitive Bayesians, or even more broadly, bad statistical thinkers? Simply, Bayesian reasoning corrects some of the issues with bad statistical thinking.

Bad statistical thinking leads to bad judgments and decisions, which have a wide variety of consequences in everyday life as well as in arenas such as politics and science. Thus, everyone should become better Bayesian thinkers, because under uncertainty, accurate probabilistic judgments are useful and important.To give a accurate depiction of how Bayesian reasoning works, let us return to the Monty Hall problem, and examine why not only switching doors matters, but that it is beneficial to switch.

When the host first opened the door with the goat, something happened: opening the door gave the player extra information, and thus changed the probability of outcomes. By utilizing this extra information, it is no longer a 50% chance for the player to win the car after switching doors, but a ~67% (2/3) chance. Let us suppose that the player picks the door which contains the car. The host opens either the first goat door or the second (it does not matter), and the player switches to the other goat door and loses.

Now, suppose the player picks the first goat door instead, which means the host is forced to open the second goat door. Since the only other door contains the car, the player switches and wins. Lastly, suppose the player picks the second goat door. The host is forced to open the first goat door, which again, means the player will win the car after a switch. These are the only three possible scenarios, and so we see that the probability of winning is two out of three if the player switches.

Conversely, what if the player doesn’t switch? In the first scenario, the player wins the car, but in scenarios two and three, the player obviously loses. Thus, to not switch is to have only a 33% (1/3) chance to win the car.The Monty Hall problem is a rather simple illustration of how Bayesian reasoning works, so in order to gain a more complete understanding, we must explore its principles.

In 1763, a paper by Reverend Thomas Bayes was published posthumously called “An Essay towards solving a Problem in the Doctrine of Chances,” and brought about a paradigmatic shift in statistics: by using ever-increasing information and experience, one can gradually approach the unknown or understand the unknown (of course, his main motive was to prove the existence of God).

Fundamentally, Bayesian reasoning believes in the correction of probabilities over time, and that all probabilities are merely estimates of the likelihood of events to occur. Through the further efforts of mathematicians like Lagrange in perfecting the Bayesian framework, we now have a modern and complete theory of probability. First, there are what we call priors, which is the strength of our beliefs, or put it another way, the likelihood that we are to change our beliefs.

Then, we have our posteriors, which is the empirical aspect, or the influx of new information. The Bayesian framework then takes these two components and mathematically analyzes how posteriors affect priors. If we know nothing about an event, then all we can do is estimate a probability. However, if there is new information, then the probability must be corrected based on this new information.

Over time, as experiences grow through more information, these estimates of probabilities will eventually fit “reality.” In the Monty Hall case, the moment the the host opened the goat door, that influx of new information, or change in posteriors, immediately influences the player’s priors. If the host doesn’t open a door, the player merely has a 33% chance to win the car between the three doors, and switching makes no difference.

However, since the host removes a door, and specifically the door that contains a goat, these two new posteriors directly influence the original prior from 33% to 66%. One might think that this method of thinking is mysteriously similar to the scientific method, which is certainly true. However, To put it another way, Bayesian thinking is how to use some known information or experience to judge or predict the unknown.

For example, event A is “rainy tomorrow” and event B is “cloudy tonight”. If you see cloudy tonight, what is the probability of raining tomorrow? If you use the Bayes theorem directly, you only need to know the probability of raining every day, the probability of cloudy nightly, and if one day it rains, then the probability of the cloudy night of the previous night will be substituted into the formula and done.

The question is, where do these probabilities come from, and how do we infer the possibility based on the information we have . In fact, most of the valuable problems are backward problems, for example: the stock market, through those few signs can be judged to be a more or less opportunity; the hospital, through which symptoms can determine what is the disease; science Research, through several experimental data, you can construct what theory to explain the model and so on.

In general, mathematicians, physicists, etc. are all about backward problems, or they can not predict or judge the outcome with few signs or phenomena, and there is no value (by the way, do not know the reverse Problem-thinking people can not fight in the financial market or the stock market. At present, the most advanced research in the speculative market is almost a process of backward stochastic process and martingale theory. It is known that the incidence of a disease is 0.001, that is, 1 in 1,000 people is sick.

There is a reagent that can test whether a patient is sick or not, and its accuracy is 0.99, which means that 99% of the patients may be positive when the patient really gets sick. Its false positive rate is 5%, which means that 5% of the patients may get positive if they do not get sick. There is a positive test result of a patient, what is the probability that he does get sick?We got a staggering result of about 0.019. In other words, even if the test is positive, the probability of getting sick is only increased from 0.1% to 2%.

This is the so-called “false positive”, that is, the positive result is not enough to show that the patient is sick.Why is this? Why is the accuracy of this test up to 99%, but the credibility is less than 2%? The answer is related to its false positive rate. Here we see the power of the Bayesian theorem, that it allows us to deduce the unknown probability from the known probability and the information at hand.The human brain and quantification vs heuristic thinking.

The advantage of Bayesian analysis is that it does not require any objective estimation, just guess a priori casually. This is the key, because most of the events that occur in the real world have no objective probability. This is actually very similar to the scientific method: we did not know anything from the beginning, but we are willing to experiment and gradually find out the laws of nature. Bayesian reasoning operates in the same way, through continually the posterior probability in accordance with existing experimental data.

Biggest problem with Bayesian reasoning is that human brains cannot quantify information easily. The most commonly raised example is Malcolm Gladwell’s “Outliers”, where many people who are trained enough in certain low-chaotic environments make correct decisions and judgments without using the Bayesian framework at all. Firefighters, for example, do not undergo a Bayesian calculus before deciding whether or not it’s safe to pull a child out of a burning building.

They just do it because they’ve done it many times before, and have a rough heuristic estimate on the safety of such an action. Similarly, chess players do not use Bayesian analysis to think many turns ahead; what research has found is that through thousands of hours of practice and becoming familiar and experienced with similar setpieces in the past, gives them an ability to predict moves assuming that the opposing player is also rational.

Conversely, high chaotic environments, such as the political sphere, is where Bayesian reasoning thrives due to the high amount of uncertainty.The other criticism are from the frequentists. In general, the probability of teaching in school can be called frequencyism. An event, if performed repeatedly multiple times independently, dividing the number of occurrences by the number of executions yields a frequency.

For example, throwing coins, throwing 10000 times, 4976 times positive, the frequency is 0.4976. Then if the implementation of many many, the frequency will tend to a fixed value, is the probability of this time. In fact, to prove it involves the central limit theorem, but it does not start.