Our comprehensive science extension textbook will help you excel in science extension. As always we have provided questions and answers at the end of every chapter – doing well in science extension isn’t about how much you read but how many questions you answer and confidently get correct. This open access text book can also act as your science extension study notes.
Science Extension Textbook contents page
- Module 1: The foundation of scientific Thinking
- Chapter 1: Development of modern scientific research
- Chapter 2: Current influences on scientific thinking
- Module 2: The Scientific Research Proposal
- Chapter 3: Developing the Question and Hypothesis
- Chapter 4: Preparing data for processing
- Module 3: Data and evidence
- Chapter 5: Describing trends and patterns in data
- Chapter 6: Statistical analysis
- Chapter 7: Modelling with data
- Chapter 8: Scientific decision making
- Chapter 9: Making decisions with data
Science is comprised of the sum of humanities scientific understanding. Science has been a tool used by and added to by humans for millions of years. Science’s aim is to describe the entire nature of reality. This is a dynamic understanding which can be lead by technology
Module 1: The foundation of scientific Thinking
Science has four main concepts; scientific laws, theories, hypothesis and observation – one of these can not develop into another they are separate entities. However, these are all integral parts of the scientific method. The scientific method is the process through which new laws, theories, hypothesis and observations can be added, removed or replaced into our scientific understanding. Science is dynamic and the discovery.
While reading our science extension textbook, we want you to keep the following in the back of your mind.
Scientific Laws – a description of a particular phenomenon, a scientific law does not explain phenomena rather it describes it. These are not absolute and can be replaced by new observations. For example, Newton’s law of universal gravitation:
Scientific Theories – an in-depth explanation of a particular phenomenon, these can also be replaced and improved but are the widely accepted explanation for a phenomenon. These must-have passed the rigour of scientific testing. For example; Einstein’s theory of general relativity, collision theory, the theory of plate tectonics and theory of natural selection.
Scientific Hypothesis – a falsifiable idea or proposition to explain/ predict a certain phenomena which is less in-depth than a theory which can be tested. This can not be proved correct, however, evidence can be found which failed to disprove it. For example; the Michelson-Morley experiment hypothesised the speed of light would be different depending on the direction of the ether wind
Scientific Observations – the knowledge regarding the nature of reality derived by our senses. The epistemological view of scientific observation will be investigated further later on. For example; when the ball was dropped it accelerated towards the earth.
Absolute Truths – regardless of the time or location the truth is the same – it can not be changed. There are very few absolute truths that exist in science. For example, a square never as round corners
The Development of Modern Science
Since science is the progression of idea’s to attempt to explain the nature of reality more effectively than previous attempts it has developed throughout the ages.
Chapter 1: The foundation of scientific Thinking
In this chapter of the science extension textbook, you will look at what scientific thinking is. You will consider questions such as how do we know what we don’t know? Then you will be prepared to analyse how philosophy has influenced the development of science.
Inquiry question: How have philosophical arguments influenced the development of modern scientific research?
Philosophical arguments can use both inductive and deductive reasoning to attempt to evaluate the basis of a particular conclusion. These philosophical arguments forced scientist’s conclusions to pass the rigour of scientific testing in order to explain phenomena. The conclusion had to be drawn from a valid experiment and the explanation for observed phenomena had to pass Occam’s razor.
Explore epistemology and alternative ways of knowing, for example, the development of navigation
What does it mean to know something?
To simplify this question we will look at the knowledge of a fact, this could include who, what, when and where of a certain detail which is true. Therefore, knowledge is connected to a fact.
“To Know” is a universal word which can be translated in every language – this is very rare for language only about 100 words are known to do this.
Epistemology is knowledge acquired through the senses such as sight, feel, sound or taste. In order to know something through epistemology, one requires confidence in what they are observing through their senses to be correct, this, therefore, requires them to believe what they are seeing is correct and also requires that what they are perceiving to be a truth. Whilst, some belief knowledge to be a social construct which is based on what humans deem to be the “truth”.
One theory of epistemology is that knowledge exists before it is discovered, therefore, it’s out there waiting to be discovered. Whereas on the other hand it is believed that knowledge is created through discovery by man.
From here the question arises “How do we know what we don’t know?” Whilst this is still being debated, however, the role of science is to discover what is not known
Other ways of knowing include but are not limited to:
Ontology is an understanding of the nature of being human. This may be the view that people are best represented as individuals whilst others may believe people are best represented as groups. Research’s beliefs of the ontology may influence their research for example if they believe the former they may conduct personality tests whilst if they believe the later they will conduct observational investigations to understand how they interact with others.
Axiology aims to understand the value of research. This can be trying to determine what is beneficial research i.e is researching global warming or a new treatment for cancer more valuable – this can be influenced by social, cultural, political and economic factors. This can also influence whether researches want their research to influence society or to better understand society.
Therefore, epistemology, ontology and axiology are underlying philosophical ways of knowing which are usually used by research’s subconsciously during a scientific investigation. There own view’s attribute to the way they conduct their methods.
- Want more detail? Influence of epistemology and alternative ways of knowing
Outline epistemology, axiology and ontology[1 mark each]
Evaluate how our understanding of epistemology reveals the limitations of our scientific understanding and our ability to understand the world. [9 marks]
Describe the influence of empiricism on scientific inquiry
Science is influenced by people’s perception of reality because this is what science aims to explain and understand. Empiricism is a theory whereby all knowledge is gained through the senses so our perception of reality is gained only through our senses according to this theory. Using empiricism to answer the epistemology question “how do we know what we don’t know?” reveals that scientific investigation is influenced by what we observe of reality and attempt to explain with hypothesis’ then theories when applying the scientific method. Therefore, empiricism can be a source of an inquiry question which can direct a scientific investigation.
For example, observing the wings of a Beatle through the senses of sight. This empirical observation could lead us to inquire “what is the function of the wings of a Beatle?” Which results in an experiment where it is observed a Beatle with wings is capable of flying but the same type of Beatle without wings (genetically identical clone which is genetically engineered not to have wings for a more ethical study to reduce suffering) is unable to fly. Hence the function of the wings was discovered to be for flight hence emperiscm shaped scientific inquiry.
It should be noted that other ways of knowing can lead to the development of new scientific ideas. For example, thought experiments such as Einstein’s thought experiment to explain the phenomena of simultaneity with a speeding train lead to the inquiry and production of the theory of special relativity.
Therefore, empiricism is one possible way to generate scientific inquiry questions to study the nature of reality.
Compare induction and deduction with reference to scientific inquiry
For more in depth lessons on top of the science extension textbook, we offer smaller lessons.
From our past experiences, it has been observed that there are phenomena which reoccur the same way as previously.
Induction is the process through which past experiences can be used to determine or predict a future outcome. We use these for our day to day activities. This could be as simple as buying a close friend a type of tea such as an English breakfast tea. Previous specific past encounters with this person allowed you to generalise they liked English Breakfast tea. From these specific encounters, you were able to generalise that they always like English Breakfast tea i.e so you bought them this type of tea. You cough when you eat peanuts. These are both examples of inductive reasoning.
However, inductive reasoning can be incorrect – even though it seemed logical that past encounters they had English Breakfast tea so future encounters they will have tea – you may have only ever met them in the morning before and now you were buying them a tea in the afternoon when they drink coffee. Hence specific previous past cases is not always the best way to formulate a general rule.
Therefore, inductive arguments are only likely to be true if the string of arguments used to make the point is logical. From this paradox’s can occur.
“Nelson Goodman’s famous grue paradox (Goodman 1955, 73–75). (See (Norton, 2006), (Olson, 2006) and the entry on formal learning theory for recent commentary on the paradox.)
Suppose that at time t we have observed many emeralds to be green. We thus have evidence statements
Emerald a is green,
Emerald b is green,
and these statements support the generalization:
All emeralds are green.
But now define the predicate “grue” to apply to all things observed before t just in case they are green, and to other things just in case they are blue. Then we have also the evidence statements
Emerald a is grue,
Emerald b is grue,
and these evidence statements support the hypothesis
All emeralds are grue.
Hence the same observations support incompatible hypotheses about emeralds to be observed in the future; that they will be green and that they will be blue.
A few cautionary remarks about this frequently misunderstood paradox:
- No one thinks that the grue hypothesis is well supported. The paradox makes it clear that there is something wrong with instance confirmation and enumerative induction as initially characterized.
- Neither the grue evidence statements nor the grue hypothesis entails that any emeralds change color. This is a common confusion; see, for examplem Armstrong 1983, 58; and Nix & Paris 2007, 36).
- The grue paradox cannot be resolved, as was the raven paradox, by looking to background knowledge (as would be the case if it entailed color changes). Of course we know that it is extremely unlikely that any emeralds are grue. That just restates the point of the paradox and does nothing to resolve it.
- That the definition of “grue” includes a time parameter is sometimes advanced as a criticism of the definition. But, as Goodman remarks, were we to take “grue” and its obverse “bleen” (“blue up to t, green thereafter”) instead of “green” and “blue” as primitive terms, definitions of the latter would include time parameters (“green” =def “grue if observed before t and bleen if observed thereafter”). The question here is whether inductive inference should be relative to the language in which it is formulated. Deductive inference is relative in this way as is Carnapian inductive logic.
Using Godman’s example inductive reasoning can contain contradictory statements which may appear to be flawed but lead to an incorrect or illogical conclusion. Therefore, inductive reasoning is more effective for drawing a likely conclusion, however, you can not be certain that it will be true.
Abduction is also a commonly used method to draw a conclusion. This is used more so by Doctor’s peacing together symptoms to establish a probable illness and detectives coming to an explanation which explains all the evidence they gathered – this is what Sherlock Holmes used to draw a conclusion. Abduction is finding the best explanation for the evidence at hand. This is done by eliminating the lesser theories to arrive at the best explanation. The conclusion drawn by abductive reasoning can still be incorrect like inductive reasoning, however, it is more likely to be correct then inductive reasoning.
Deductive reasoning is always true. It is making a specific prediction from a generalised understanding. For example, the generalised statements; all fruits have seeds and all mangoes are a type of fruit – from this, a deductive conclusion is that all mangoes have seeds. This is true.
Assess parsimony/Occam’s razor and its influence on the development of science
Occam’s razor is a heuristic used throughout the ages but named after William of Occam, a Franciscan Friar from the 14th century. Occam’s razor is applied to find parsimony which is the most simplistic explanation for a particular phenomenon. It is a razor since it wishes to shave off excess on not necessary explanations for a phenomena. Therefore, Occam’s razor can be used in science to decide which of two competing theories should be accepted to explain phenomena. For example, the heliocentric vs geocentric model to explain the orbits of planets and the sun. Since the heliocentric model was more simplistic by positioning the planets to orbit the sun as opposed to the earth, as a result, making it the most widely accepted theory.
Occam’s razor is used in scientific inquiry whereby the scientific method attempts to find the simplest explanation for a phenomenon. For example, in taxonomy the simplest explanation for the evolutionary progression of a phylogenetic tree.
Occam’s razor can be limited since arguments for two competing theories may make it difficult to decipher which is the simpler theory. This is evident in the argument for intelligent design vs chance event for the creation of life. It could be argued the simplest explanation for the extravagant complexity of living things is due to a supernatural creator. However, the counter-argument supporting life being a chance event is evoking a supernatural dimension increases the complexity of their argument thereby making chance event a more simplistic explanation. Hence Occam’s razor can be limited when it is difficult to decipher the more simplistic explanation.
Since Occam’s razor is a heuristic technique the outcome by it may not be the optimal result, for example, the helical model of the universe was later strengthened by Kepler’s first law which states all planets orbit the sun in an elliptical orbit with the cun being at one of the focal points. Therefore, Occam’s law has allowed science to develop taking the simplistic theory over the more complicated when both are equally as effective at describing a phenomenon – it has in no way guaranteed the optical explanation.
Analyse the importance of falsifiability in scientific research
This is a more in depth lesson building on what you will cover in the science extension textbook.
Falsifiability is the process through which something can be disproved, this is integral to all scientific investigations. It was first introduced by the philosopher, Karl Popper. A hypothesis must always be falsifiable since science is the best explanation of known phenomena. One must always try to fit our scientific understanding to match reality and disregard or improve the theory if it is found to be incorrect. This prevents future attempts from investigating something which has no grounding in reality.
The hypothesis that “all frogs are green” is falsifiable since a frog can be found that isn’t green thereby disproving the hypothesis. However, saying that “All ghost frogs, which people can’t see are green” is a non-falsifiable hypothesis whereby a scientific enquiry can not be conducted since there is no way to find a ghost frog since we can’t see them. Therefore, it is impossible to find any coloured ghost frog to prove the hypothesis as being wrong.
Pseudoscience is something which is not falsifiable and for a theory/ hypothesis to be considered scientific it must be falsifiable. I.e an observation can disprove it.
Therefore, falsifiability is integral to scientific research since science can only disprove theories. Whilst evidence can be found which supports a theory, e.g. increase in reaction rate when the temperature of a system is increased supporting collision theory, we are unable to prove something as correct. Thereby making an unfalsifiable hypothesis immune to the rigour of the scientific method preventing people from disproving it.
Therefore, modern day science should be falsifiable, testable and irrefutable.
Evaluate the significance of confirmation bias, including theory-dependence of observation
Confirmation bias must always be avoided during a scientific experiment since it can significantly stagnate scientific research. Theory- dependence of observation is when previous knowledge of what is expected to happen is what is observed. Empiricism is detecting the world through our senses – our own interpretation of results can be influenced by previous theories or what is expected to occur.
Confirmation bias was avoided in the gold foil experiment which allowed Rutherford to create a paradigm shift from Plumpudding’s model of the atom to Rutherford’s model. This was done by positioning radiation detectors the entire circumference of the gold foil. Therefore, he could identify alpha particles as being deflected, reflected and passing straight through hence he could identify the atom as not entirely free space. This is an example of new discoveries in science by avoiding confirmation bias. Had Rutherford have had theory dependence confirmation bias he would have been satisfied with the detector only being in front of the gold foil hence missing the evidence that would reveal the atom has a positive nucleus which reflected the radiation.
Therefore, in science it is vital we perform experiments objectively and free of bias in order to extend our own scientific understanding.
Evaluate the contribution of cultural observational knowledge and its relationship to science, Using historical examples.
After 49000 BCE, exemplified by Aboriginal cultures
- The process of preparing cycad seed through leaching the water-soluble toxins in an open stream to remove cycasin. Aboriginal people tested the effectiveness of the treatment for making bread by feeding to the elders and varying method accordingly.
- Wordy Yong Stone arrangement maps different setting positions of the moon throughout the year. This allowed them to preempt when certain foods would be available – this is the first known arrangement of rocks to creating
- Variability of red giant pulsating stars was first observed in 1596, however, this has been cultural knowledge for 10000’s of years.
Before 1500 CE, exemplified by Greek cultures
- Aristotle’s contribution to the field of logic – he founded deductive reasoning
- Archimedes contributed numerous simple machines such as the Archimedes spiral
Before 1500 CE, exemplified by Egyptian cultures
- The development of a ship sale using principles of aerodynamics and later net force to determine a ship could be sailed with side wind
- The use of papyrus to record scientific observations and thoughts
Before 1500 CE, exemplified by the Asia region
- Han Dynasty rice paddy farms
- Han dynasty seismograph
Note in this science extension textbook we have chosen multiple examples but the syllabus specifies:
Select one example from the following list to analyse the paradigm shift and how evidence is used to support new theories to explain phenomena and their consequences.
A paradigm shift is a change in scientific thinking. This is created through new evidence or a new theory to explain a phenomenon which was not fully understood previously.
Lavoisier and oxygen
Before Lavoisier, all elements were believed to contain “phlogistion”. Phlogiston theory stipulated that phlogiston was a fire-like element found in all combustible bodies. Metals were believed to contain low amounts of phlogiston whilst hydrocarbons were believed to contain high amounts of phlogiston. When these “elements” were burnt they decomposed into phlogiston and ash. Lavoisier established that this process was combustion which required oxygen to occur. He also used Priestly’s mice in a bell-jar experiment to identify the fuel needed for something to combust was also used for animals to breathe – internal combustion. Therefore, Lavoisier’s work allowed for the discovery of oxygen creating a paradigm shift in scientific thinking.
Einstein and general relativity
Wegener and continental drift, leading to plate tectonics
McClintock and transposable elements, commonly known as ‘jumping genes’
Chapter 2: Influences of current scientific Thinking
In this chapter of our science extension textbook we are going to look at the influences that direct contemporary scientific thinking. You should also keep in your mind how you think current scientific thinking influences the world around you.
Inquiry question: What currently influences scientific thinking?
Analyse the current influences on scientific thinking, including economic, political, global, cultural, environmental issues and current social issues social
As explored earlier axiology is the philosophical study which determines what we perceive as valuable. is greatly influenced by a number of factors which affects our perception of the value of certain lines of scientific inquiry based on the potential upside of the experimentation. However, current scientific thinking is also greatly influenced by past scientific thinking.
A hypothetical scientific study into a new genetically modified crop will be greatly influenced by the above issues. The research will be required to have the potential for large economic gain, the profit from the research must exceed the expense – this could mean the crop must have a high yield or being important enough in the other area’s to warrant an economic loss. The crop may also be required to be drought tolerant, especially during the current drought Australia is experiencing – what good would it be to engineer a crop which will die from a shortage of water? Additionally, the current social influence would negatively affect the study since their is skepticism regarding the reliability of GMO crops. Public resistance would also limit people from buying it hence making it less economically effective.
Therefore, it is clear that economic, political, global, cultural, environmental and social influences are interconnected and are vital in determining the value of research.
Analyse the influence of ethical frameworks on scientific research over time, including human experimentation, experimentation on animals, biobanks and the use of research data
Human ethical frameworks have drastically changed over time moving towards favouring anonymity and privacy as well as consensual and willing recipients. Over the past six decades research ethics have required the research to have merit (determined by its axiological value), beneficence (benefiting the participants) and respectful to the participants. It should also be carried out with respect and integrity.
The Nuremberg code of medical ethics was developed after Nazis experimented throughout the holocaust on Jews, POWs and other persecuted individuals. Experiments were performed such as attempting to change the genetic code of two genetically identical babies and other experiments with transplanting bones, organs and nerves. This experimentation is today considered highly unethical and inhumane with the participants dying, becoming disfigured or permanently disabled.
Experimentation on animals is currently requires the three R’s. The science extension textbook recommends you never conduct animal trials in high school due to the large amount of paperwork and ethical considerations that are required. These include;
Reduction of the number of animals in the research.
- For example using 3 animals instead of 10
Replace animals where possible with suitable replacements.
- For example using ballistic gel instead of pig’s if testing the penetrating ability of a bullet
Refinement of the pain the animals will experience
- For example, providing the animals with anesthetic before surgery
Animal ethics have also had a great deal of improvements.
Biobanks are repositories of genetic information and biological samples. These are useful for medical and scientific research. Storing genetic information of a range of species supports conservation efforts since it makes it possible for extinct animal species to be reintroduced, however, there are ethical issues around this as well. The information stored in biobanks must also be gathered by ethical means and securely stored and catalogued.
Research data must also be securely stored and encrypted, the privacy of the individuals who supplied the data must also be upheld. These individuals must also be informed as to where their research is going and what it is being used for.
Module 2: The Scientific Research Proposal
Chapter 3: Developing the Question and Hypothesis
Inquiry question: What are the processes needed for developing a scientific research question and initial hypothesis?
|Primary Source||A method is VALID if the measurements are actually measuring what you intend them to measure. it incorporates suitable equipment, controlled variables, appropriate measuring procedures etcIt is a fair test; no uncontrolled variables other then those intended||Results are RELIABLE if the experiment is repeated and the results are the same (within an acceptable margin of error)|
Reliability can be increased by increasing the validity of the experiment
|Results are ACCURATE if the design of the experiment is valid and the sensitivity of the equipment used they are close to the true value of the quantity being measured. they can be substantiated in secondary sources||Results are PRECISE ifThey can be reproduced multiple times|
|Secondary Source||The validity of evidence is increased if:they have been gathered using appropriate methods they relate to the hypothesis or problem||The reliability is increased if:the information is not biased it has been written by a qualified person it is on a reputable site, i.e. .gov .edu it is current (check date) it refers to data and statistics from valid first-hand investigations||A sources accuracy is increased if: it can be substantiated in multiple sources|
Scientific Research Proposal
Inquiry question: How is scientific research planned, based on a relevant hypothesis?
Criteria for a hypothesis
A hypothesis should be developed in response to previous research. It should be a testable and falsifiable statement which predicts the change of an independent variable and the result on the dependent variable followed with brief reasoning.
Common scaffold’s for a hypothesis include:
If [independent variable] then [depndent variable] will… because [reasoning]
If … and … then …
Inquiry question: How is an appropriate methodology developed to collect valid and reliable data?
Assess and evaluate the uncertainty in experimental evidence, including but not limited to:
– systematic errors
– random errors
To reduce random errors taking repeated measurements and averaging will reduce their effect. Where as, finely calibrating equipment and controlling all variable will reduce systematic errors.
Assess and evaluate the use of errors in:
– mathematical calculations involving degrees of uncertainty
– graphical representations from curves of best fit
compare quantitative and qualitative research methods, including but not limited to:
– design of method
– gathering of data
– analysis of data
Investigate the various methods that can be used to obtain large data sets, for example:
– remote sensing
– streamed data
Remote sensing is measuring and observing a site from detecting the emitted radiation from above e.g a satellite taking pictures of the earth. Remote sensing is sensing from a far away source it can be both analogue or digital.
Data streaming is the continuous streaming of data which is concurrently being detected/generated whilst it is being streamed e.g livestreaming bread mold growing on bread and continuously analysing this.
Propose a suitable method to gather relevant data, including large data set(s), if appropriate, applicable to the scientific hypothesis
Up next in the science extension textbook is all about turning data into evidence.
Module 3: Data and evidence
In this module of our science extension textbook you will learn how to analyse, understand and make decisions from data. Previously you have learnt that science is about understanding the world around us – we need data to do this.
Chances are you have heard of Facebooks data scientists getting paid millions of dollars to mine data – you will learn the same principles here. This way you too will be able to work with big datasets for your science extension project.
Chapter 4: Preparing data for processing
The data you collect isn’t always ready t
Inquiry question: How is data processed so that it is ready for analysis?
Investigate appropriate methods for processing, recording, organising and storing data using modern technologies
Conduct a practical investigation to obtain a qualitative and a quantitative set of data and apply appropriate methods to process, record, store and organise this data
Assess the impact of making a large data set from scientific sources public, for example:
– LHC (Large Hadron Collider)
– Human Genome Project
- It can allow citizens to feel connected to science
- Glitch in machine may be registered as new theory due to insufficient data cleaning
- Health insurance bias
- Can be used detrimentally e.g the Nazis during world war 2 had desired
Data, Evidence and Decisions
Chapter 5: Describing trends and patterns in data
This is a skill based section of our science extension textbook, it is important to analyse and communicate the scientific insights we identify in our data.
Inquiry Question: What tools are used to describe patterns and trends in data?
Analyse and determine the differences between data and evidence
describe the difference between qualitative and quantitative data sets, and methods used for statistical analysis, including but not limited to:
– content and thematic analysis
– descriptive statistics
select and use appropriate tools, technologies and/or models in order to manipulate and represent data appropriately for a data set, including but not limited to:
– graphical representations
– models (physical, computational and/or mathematical)
– digital technologies
assess the relevance, accuracy and validity of the data and determine error, uncertainty and comment on its limitations
evaluate the limitations of data analysis and interpretation
Chapter 6: Statistical analysis
In our statistical analysis chapter of the science extension textbook, we have
Inquiry question: How does statistical analysis assist in finding meaning in the trends or patterns in data sets?
- Data vs Evidence
- Qualitative Data
- Quantitative Data
- Numerical summary
- Normal Model
- Scatter Plots and correlation coefficient
- Bootstrapping and confidence intervals [Ext]
- Paired T-test
- 2 sample T-test
- Paired T-test Bug example
- Regression Testing
Describe the difference between correlation and causation
Correlation does not imply causation. Correlation arrises when two or more variables follow similar trends for example an increase in outdoor temperature correlates to an increase in use of air conditions – this doesn’t mean that using an air conditioner increases the outside temperature. Causation is the result of one variable causing another to change. An increase in concentration of enzymes causes increases the rate of reaction of the reactants (until all the active sites are used up). This is a causal relationship between reaction rate and concentration of enzymes.
To establish Causation
- All other variables must be ruled out.
- It must be logical
- Strong correlation must be present and removing one must affect the other
Explain the requirements to establish causation
The Bradford Hill Criteria to establish causation exists, however, for this course we will focus on the three following criteria:
- Correlation: if two qualitative variables a linear regression graph can be used
- Time sequence: using logic the independent variable must come before the dependent variable
- Non-spuriousness: it must be not false – there can’t be any extraneous variable e.g as children’s shoe size increases so does their knowledge
Simply put to establish Causation you need:
- All other variables must be ruled out.
- It must be logical
- Strong correlation must be present and removing one must affect the other
use available software to apply statistical tests appropriate to a large data set(s) to assist with the analysis of the data
Up next in the science extension textbook will be all about making models with our data.
Chapter 7: Modelling with data
A recurring undercurrent throughout the science extension textbook is the idea that all models are wrong but some are useful. SO why is it we bother making models? This is due to their ability to make predictions about the world around us, test our ideas and assumptions and communicate our understanding to others.
Inquiry Question: How can data modelling help to process, frame and use knowledge obtained from the analysis of data sets?
Evaluate data modelling techniques used in contemporary science associated with large data sets, including predictive, statistical, descriptive, graphical and simulations.
Data modelling is the process through which unorganised data can be organised and processed, it can then be conceptually categorised and represented to allow for clearer understanding. The data model acts as the framework which represents the relationships between different variables in the data modeling process.
Models describe and attempt to explain certain phenomena. They can allow for a simplistic and concise explanation of a phenomena, however, limitations arise when different variables are lacking and therefore they may miss certain features of reality. Additionally, models can be inaccurate and lead to incorrect conclusions whilst if they are accurate they can make for an incredibly accurate representation of reality.
Inorder to create a model, the following steps are required:
- Propose an inquiry question
- Develop an initial model to replicate the phenomena
- Investigation to explore existing knowledge, experiments and theoretical ideas
- Evaluate model
- Apply back to inquiry question
In chapter 6 of the science extension textbook we looked at all the ways you can use statistical analysis to gain insights from your data. Then we analysed how to build out models to understand our data. Next we will look at drawing insights from what we have generated.
Chapter 8: Scientific decision making
In this chapter of the science extension textbook we look at what is required to make effective decisions with the data we generate and analyse.
Inquiry Question: How is evidence used to make decisions in the scientific research process?
Compare Evidence and Data
Data is quantitative and/or qualitative representation of particular phenomena. Likewise, evidence is quantitative and/ or qualitative data which is supporting a conclusion. Therefore, data is lacking context and by extension supports no conclusion since it is unprocessed. Thereby, processing data to refine it and presenting it makes it evidence if it is supporting/ disproving a theory or hypothesis.
Assess the benefits of collective and individual decision-making
Decision-making is the process which aims to choose the most appropriate action or idea based on the evidence available. Collective decision-making allows multiple people to discuss the nuances of the problem thereby preventing the likelihood that certain evidence would be overlooked if an individual was only making the decision. This is because multiple people with different backgrounds will see problems differently so they can employ different heuristics. However, individual decision making allows for more accountability since they alone are responsible for the repercussions and consequences. This can increase stress limiting someone to work and make decisions at their peak. However, it overcomes an issue that arises in collective decision making whereby one person with more control may suppress others from adding their own judgments. Additionally, individual decision making can mean one person is better informed since they know all components of the research and can understand the links better, however, this also means they have to work harder and may take longer to come to a decision which doesn’t occur in collective decision-making. Therefore both individual and collective decision making allows for different benefits but modern science requires collective decision-making due to the breadth of modern knowledge and the faster-passed work. Individual decision making was more prolific earlier such as work by Newton, Einstein and Darwin.
Demonstrate the impact of new data on established scientific ideas such as:
- Gravitational waves on general relativity.
The discovery of gravitational waves acted as evidence to support general relativity – verifying the existence of a space-time membrane.
- Mechanisms of disease transmission and control
- Prediction of natural disasters
- Effects of chemical pollutants on climate
Chapter 9: Making decisions with data
In this chapter of the science extension textbook, we will look at making decisions with data.
How are the inferences, generalisations and conclusions derived from valid and reliable data reported?
assess methods by which scientists communicate research findings, including the scientific peer-review process, publishing in online and print journals, presenting at conferences and presentations in popular media
Literature Review and formulating an inquiry question
Inquiry question: What are the processes needed for developing a scientific research question and initial hypothesis?
— conduct an initial literature search, from one or more areas of science, to identify the potential use of a contemporary, relevant publicly available data set develop a scientific research question from the literature search formulate an initial scientific hypothesis based on the scientific research question evaluate the resources associated with the initial scientific hypothesis derived from the literature in terms of:
– the scope to perform an investigation to obtain primary data
– the availability of secondary-sourced data
– the availability of a relevant publicly available data set(s)
– reliability and validity
– assessing the current state of the theory, concept, issue or problem being considered
assess the process involved in the development of a scientific research question and relevant hypothesis
Scientific Research Proposal
Inquiry question: How is scientific research planned, based on a relevant hypothesis?
conduct a detailed literature review to support the validity, significance and appropriateness of the scientific research question
formulate a final scientific hypothesis based on the scientific research question
develop the rationale and possible outcomes for the chosen scientific research
develop a detailed plan to investigate the scientific hypothesis including:
– the overall strategy
– data analysis
– representation and communication of the scientific research
critically analyse the scientific research plan to refine and make appropriate amendments
employ accepted referencing protocols, for example:
analyse the results from the Scientific Research Project and present findings about the data set obtained as a discussion, using scientific language and peer-reviewed supporting data
analyse trends, patterns and relationships in the data set to suggest modifications to the scientific research methods employed
analyse the patterns and trends derived from the associated data set(s) relevant to the scientific research for inclusion in the report
justify choices of mode and media for the presentation of the scientific research, based on purpose, audience and context
communicate scientific and/or technical information or ideas clearly and accurately using a variety of forms appropriate to purpose, for example orally, mathematically, graphically or in writing
justify future directions of further scientific research outlined in the Scientific Research Report
use scientific language in the analysis and evaluation of an area of scientific research by means of a scientific report, supported by an associated portfolio of evidence
Conduct an investigation to access and obtain relevant publicly available data set(s), associated with the proposed hypothesis, for inclusion in the development of the Scientific Research Project
analyse patterns and trends arising from the data set(s) related to the Scientific Research Project to:
– construct a relevant conclusion
– suggest possibilities for further investigation
The following is a glossary of terms used through the science extension textbook.
|Aboriginal and Torres Strait Islander Peoples||Aboriginal Peoples are the first peoples of Australia and are represented by over 250 language groups each associated with a particular Country or territory. Torres Strait Islander Peoples whose island territories to the north east of Australia were annexed by Queensland in 1879 are also Indigenous Australians and are represented by five cultural groups. An Aboriginal and/or Torres Strait Islander person is someone who: is of Aboriginal and/or Torres Strait Islander descent identifies as an Aboriginal person and/or Torres Strait Islander person, and is accepted as such by the Aboriginal and/or Torres Strait Islander community in which they live.|
|content analysis||Strategies used to analyse the characteristics of a resource.|
|correlation||Interdependence of variable quantities.|
|cultural observations||Traditional knowledge, based on observations linked to local culture and to the predominant philosophy and rituals of the time.|
|data cleansing||Detecting and removing errors and inconsistencies from data in order to improve the quality of data (also known as data scrubbing).|
|data set||An organised collection of data.|
|deduction||Deriving a conclusion by reasoning using known facts.|
|descriptive statistics||Statistics that quantitatively describe or summarise features of a collection of information.|
|empiricism||The theory that all knowledge is based on experience derived from the senses.|
|epistemology||A branch of philosophy that investigates the origin, nature, methods and limits of human knowledge.|
|ethical framework||A set of codes that guides behaviour.|
|falsifiability||The idea that a theory, hypothesis or statement must be testable and shown to be incorrect, if it is to be considered scientific.|
|hypothesis||A statement that relates an independent variable to a dependent variable in a causal relationship that can be tested.|
|induction||Inference of a generalised conclusion from particular instances.|
|large data sets||Data sets that must be of a size to be statistically reliable and require computational analysis to reveal patterns, trends and associations.|
|model||A representation that describes, simplifies, clarifies or provides an explanation of the workings, structure or relationships within an object, system or idea.|
|Occam’s razor||The proposition that where multiple conclusions are found to be equally possible, the simplest is the true conclusion.|
|parsimony||Adoption of the simplest assumption in the formulation of a theory or in the interpretation of data, especially in accordance with the rule of Occam’s razor.|
|philosophical argument||A series of statements used to present reasons for accepting a conclusion.|
|primary sources/primary data||Information created by a person or persons directly involved in a study or observing an event.|
|qualitative||Relating to, measuring, or measured by the quality of something.|
|quantitative||Relating to, measuring, or measured by the quantity of something.|
|secondary-sourced investigation/data||An investigation that involves systematic scientific inquiry by planning a course of action and sourcing data and/or information from other people, including written information, reports, graphs, tables, diagrams and images.|
|thematic analysis||Analysis which converts qualitative data into quantitative data.|
About the author
Joshua Mills has a bachelor of Adv Science (medical science and medicinal chemistry) from USYD. He founded Edzion.com in 2019 – an online complimentary education company to support and inspire high school science students. Currently he’s undertaking higher degree research, working in a team on a therapeutic to treat children’s bone cancer. In his spare time, he runs ultra-marathons.