Science Extension Notes Year 12 PDF
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This document provides notes on the development of modern science, exploring topics such as the influence of philosophical arguments on scientific research, epistemology, knowledge justification, and the limitations of scientific knowledge. It also examines different ways of knowing, navigation, empiricism, rationalism, induction, deduction, Occam's Razor, falsifiability, confirmation bias, and the importance of cultural observational knowledge in science.
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Module 1: The Foundations of Scientific Thinking The Development of Modern Science Inquiry Question: How have philosophical arguments influenced the development of modern scientific research? Students: E xplore epistemology and alternative ways of knowing, for example t...
Module 1: The Foundations of Scientific Thinking The Development of Modern Science Inquiry Question: How have philosophical arguments influenced the development of modern scientific research? Students: E xplore epistemology and alternative ways of knowing, for example the development of navigation: ❖ L aw: A description of scientific phenomen or relationship between thing in nature. Eg. newton’s laws, mendels law of independent assortment. ❖ Theory: An explanation of scientific phenomenon. Eg. theory of relativity, chromosomal inheritance. ❖ Hypothesis: A limited explanation of a phenomenon; a prediction of the effect of changing an independent variable on the dependent variable. ❖ Epistemology: “Branch of philosophy that deals with what knowledge is, how we come to accept some things as true, and howe we justify that acceptance.” ❖ Knowledge is justified, true belief. The person must be able to justify the claim. It must be true, and the person must believe in it. ❖ Justifying a belief is done by using logical, reasonable and good quality evidence. ❖ Scientific epistemology involves three aspects: Qualities of scientific knowledge: ➔ Science attempts to explain natural phenomena ➔ Scientific knowledge is represented as laws/theories. ➔ Requires rrevison ➔ Evidence cannot establish a scientfiic hypothesis, but can falsify it. ➔ Its a part of social/cultural traditions of human societies. ➔ Affected by social and historical setting. Limitations of scientific knowledge: ➔ Doesn’t make moral judgements. ➔ Does not make aesthetic judgements, i.e. is Mozart’s music more beautiful than Bach’s? ➔ Doesn’t prescribe how scientific knowledge should be used. Eg. use of embryonic stem cells for healing. ➔ Doesn’t explore supernatural phenomena, such as religious ideas. How scientific knowledge is generated: ➔ Observations, experiments, rational arguments ➔ Slow and incremental steps (evolutionary progression), and giant leaps of understanding (revolutionary progression). ➔ O bservations are theory dependent., this influences how scientists obtain and interpret evidence. ➔ Knowledge can be obtained through many differen methods including inductive processes (generaliisations or deductive processes (deriving). ❖ Alternate ways of knowledge include: Emotion Faith/beliefs Imagination Intuition Memory Sense perception ❖ Navigation: Early explorer relied on sense perception to observe landforms, wind/speed direction tides and measures of distance for navigation. THis is observational knowledge. Celestial navigation used position of stars, constellations and the sun as navigational aids. Travel was often restricted to short distances or coastal areas. Advances in navigation, specifically measuring techniques and geometry has allowed the creation of accurate maps. The altitude of the north star provided latitudinal information. These are examples of knowledge constructed through memory, language (communication via oral stories/maps/accounts) and reasoning. Later, navigational instruments extended the powers of sense perception. THe compass was used to orientate travellers to the magnetic north. Other instruments such as extant, chronometer and chip log identified location in 3D space. Modern navigation uses radar, GPS, etc. Polynesians used natural navigation aids, eg. stars, ocean currents, wind patterns. They used stories and songs to memorise properites of stars islands and navigational routes. Describe the influence of empiricism on scientific inquiry: ❖ E mpiricism: “True knowledge is primarily founded on input from our senses.” ❖ When beliefs/claims are justified, we must refer to experience and observations over traditions and ideas. ❖ Empircism has influenced moden science, as we now believe knowledge should be empirically test rather than just though thought experiments or rational calculation. ❖ Rationalism: “Rationalism emphaises reason, rather than experience and observations, as the primary basis for justifying beliefs and claims.” Research results are verified primarily by reasoning. The rational/logical human mind is the source for new knowledge, not the world around us. ❖ Natural philosophy relates to explanations of the natural world. ❖ In 15th century, philosophers began to redefine how knowledge of natural world should be constructed. THis was the beginning of science. In 19th century, philosopher Willian Whewell coined the term science to describe natural philosophy inquiries. THis eventually become distinct from philosophy. ❖ Example of common roots: highest research degree is doctor of philosophy (PhD) ❖ Empiricism emphasises prior experience relating to sensory observation. ❖ Sensory information has been extended to collection through instruments ❖ O bservations are important for knowledge construction - information from observations eventually become theories (explanations for natural phenomena). Overtime, evidence and explanations become knowledge. ❖ Empiricism was crucial for the separation of natural philosophy from other philosphies to become science. It define modern science. Most knowledge is empirical. Empicisim demands knowledge is based on evidence and tested through experiments/observation. Compare induction and deduction with reference to scientific inquiry: ❖ Deduction: “The formation of a conclusion based on generally accepted statements or facts.” ❖ Induction: Formation of generalisation based on whats known/observed. Assess parsimony/Occam’s razor and its influence on the development of science. ❖ O ccam’s Razor (principle of parismony): More straight forward explanations are generally better. I.e. if two possible theories fit available evidence, the best one has fewer moving parts. ❖ Science works with competing ideas. When trying to explain natural phenomena there may arise multiple plausible hypotheses. In this situation, Occam’s Razror is useful. ❖ Historical examples: Eg. The belief in the simpler earth centred geocentric solar system model. This was replaced with the heliocentric model. The heliocentric model required complicated features such as epicycles to explain phenomena, while the heliocentric model was simpler. ❖ Occam’s razor is not exclusively used when accepting ideas, the most important factor is evidence. Other considerations: Are some Ideas more testable? Do some ideas fit better than existing ideas? Are some ideas better at producing broader explanations? ❖ Future research may falsify hypotheses based on occams razor. Cannot select from equally plausible hypotheses Analyse the importance of falsifiability in scientific research: ❖ F alsifiability is a method of developing scientific knowledge. IT involves deductive reasoning. IT claims all scientific ideas should by falsifiable through testing i.e. experimentation. If it cannot be falsified, its not scientific,. ❖ Eg. You cannot test the fact of creation science and God’s intelligent design so its not considered scientific. ❖ Falisfication separates scientific and non scientific ideas. ❖ Given ruse to a method of testing and veriffiying scientific ideas - hypotheses testing. ❖ Hypotheses are tentative explanations fo a narrow set of related phenomena. ❖ Hypotheses cannot be proven true. They can only be rejected, if evidence doesnt support them, or failed to reject. E valuate the significance of confirmation bias, including theory-dependence of observation: ❖ Inferences can be influenced by confirmation bias and theory lawn observation: C onfirmation bias: Tendency to search or interpret information in a way that confirms preconceptions. Can lead to cherry picking evidence and statistical errors. Eg. einstein believed universe was static and not expanding. Failed to notice his mathematical error when applying theory of general relativity to describe the universe, as it supported his belief. His bias caused him to accept his theory of relativity, confirming his understanding of the nature of the universe. Theory dependence of observations: Observations are theory dependent if the content, generation or scientific acceptance relies on understanding a theory. Scientific theories influence inferences and interpretations we make from observations. Eg. X-ray image is informative to a radiologist but not an untrained person. The radiologist may identify conditions because of prior exxperience/learning and utilisjng scientific theories. Background knowledge, historical and social context affect interpretations. Knowledge and theories are required to use equipment and choose appropriate observations to use. Theory-ladeness: Theory and observation should be distinct to ensure a trua and fair test. Since scientific method basis is objective observations. Observations depend on understanding how the world operates, which can introduce bias. U se historical examples to evaluate the contribution of cultural observational knowledge and its relationship to science, including: -Post-49000 BCE, exemplified by Aboriginal cultures: ❖ Indigenous people observed movement of stars to make calendars. These contained more detail than european calendars and allowed indigenous groups to coordinate movements across australia. ❖ Indigenous records were oral, so colonisation has led to a rapid loss of these calendars/traditions. ❖ Firestrick farming. Encouraged growth, burnt vegetation, reduced firefuel, lured grazing animals for hiring. ❖ Indigenous peoples had 60k+ years of climate record and knowledge which has been useful in maintaining Australian flora and fauna. -Pre-1500 CE, exemplified by Greek and Egyptian cultures and those of the Asia region: ❖ Chinese discovery of antimalarial compound 'artemisinin' in 300 AD. A literary work suggested preparations of this herb may protect against malaria. One individual extracted the active ingredient from this plant to develop the substance as a therapeutic. ❖ IT was isolated and shown to be effective against malarial parasites. S elect 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: ❖ P aradigm theories are general theories thaat provide scientists in specific fields aiwht a broad theoretical framework - Kuhn calls it the conceptual scheme. It provides them basic assumptions, key concepts and methodology in experiments. It provides research with a general direction adn goals. ❖ “It represents an exemplary model of good science within a particular discipline.” ❖ Paradigm shifts occur when one paradigm theory is replaced with another. -Lavoisier and oxygen: ❖ Historical theory was that phlogiston was contained within every flammable substance and was what caused fires to arise. ❖ Antoine Lavoisier disproved phlogiston theory by showing combustion required a gas (oxygen), and this gas hhas weight. He did this by burning elements in closed containers. Solids gained weight while the mass of the container diddnt change. The pressure inside the vessel changed, as when he opened the container, air rushed in and the weight of the vessel increased. Thus instead of giving off phlogiston, oxygen was being used in combustion. This thus disproved pholigston theory. -Einstein and general relativity: -Wegener and the continental drift, leading to plate tectonics: -McClintock and transposable elements, commonly known as ‘jumping genes’: Influences on Current Scientific Thinking Inquiry Question: What currently influences scientific thinking? Analyse the current influences on scientific thinking, including but not limited to: -Economic: ❖ Economic influences are the impact of financial resources, economic policies and market demands on scientific research/development. ❖ The US department of energy’s sunshot initiative aimed to make solar energy cost-competitive by 2020, thus it dun -Political: -Global: A nalyse the influence of ethical frameworks on scientific research over time, including but not limited to: -Human experimentation: ❖ Human experimentation uses humans (excluding social science and education) in scientific studies. ❖ It may be observational or involve manipulation, i.e. a clinical trial. ❖ Although knowledge of human biology has advannced significantly through human experimentation, many of the past studies are now considered unethical. ❖ F or example, Edward Jenner started the basis for vaccination (which was known as variolation). He took a sample of cowpox from one of hs milkmaids, and inoculated it into his son. Months later he injected his son with variola virus, and he survived. This was how he tested his theory, but is now considered unethical. ❖ Human experimentation in Australia is governed by ethical frameworks developed by national health and medical research council NHMRC. The main principles are: Respect welfare of subjects. The benefit of the research must justify any risks of harm or discomfort, and this should be minimised. Participants should not be exploited but rewarded, ad recruiting should be fair. THe method used should be scientifically sound. ❖ Human research must be approved by human research ethics committee. -Experimentation on animals: ❖ Dolly somatic cloned sheep ❖ Ivan pavlov behavioural conditioning with dogs and gastric fistula -Biobanks: ❖ Stores of biological samples, usually human. Allows sample sharing betweens scientists and is useful for genetic disease studies. ❖ Ethics involve ownership, rights and consent of donor’s materials. Material is often stored for furutre research. ❖ Intentions with biological samples must be clearly stated before experimenting. ❖ Eg. umbilical cord blood banking, as it is a source of stem cells and may be used in certain cancer treatments, and newborn heel prick screeening for genetic disease, eg. PKU. ❖ Svalbard global seev vault stores plant smaple to prserve biodiversity. ❖ -Use of research data: ❖ Research ethics: Procides guidelines for responsible conduct of research. It educates and monitors scientists conducting research to ensure high ethical standard. ❖ Honesty: Researchers must honestly report data and results, methods and procedures, and publication status. Falsifiyy or fabricating data is unethical. ❖ Objectivity:Aim to ensure objectivity and eliminate bias in experimental design, data analysis, peer review, grants, expert testimony, personnel decisions, aspects of research. ❖ Integrity ❖ Honesty: Uphold agreements ❖ Caution: Avoid carless arrows and negligence. Carefully/critically examine your own and peer’s work. Record research activities. ❖ Transparency: Share data/results/ideas/tools/resources. Be open to criticism. ❖ Respect intellectual property. Dont use any aspects of unpublished research without permission and give credit where necessary. ❖ Protect confidential communication and information such as patient records or human participant info. ❖ Publish both positive and negative results, as the aim is to advance scientific research and not just your own career. Avoid wasteful and duplicative publication ❖ Do not discriminate against colleagues or students on any basis not related to scientific competence and integrity. ❖ S trive to promote social good and prevent/mitigate social harms through research, public education and advocacy. ❖ Obery relevant laws and policies. ❖ Respect and care for animals, dont conduct unneccesarily harmful or poorly designed research. ❖ Minmise harm and maximise benefits in human subjects. Respect dignity, privacy and autonomy and declare all uses of data. Module 2: The Scientific Research Proposal Developing the Question and Hypothesis Inquiry Question: What are the processes needed for developing a scientific research question and initial hypothesis? C onduct an initial literature search, from one or more areas of science, to identify the potential use of a contemporary, relevant publicly available data: . Identify 2 or 3 areas of interest in science. 1 2. Perform multiple literature searches to identify the well-understood aspects of this research area, and what the current research is. This is easiest done by finding more recent review articles from reliable sources i.e. published in specialised journals. Develop a scientific research question from literature search: ❖ S cientific research question: A clear, specific question with a singular major focus, that is developed from a review of scientific literature. Research questions require an analysis of data and of the issue to answer, are written using scientific terminology, and direct the course of research. ❖ A good research question should: Clearly state what the researcher is doing/needs to do. Not be too broad/vague. ➔ Eg. “Discuss the connections between the Haitian earthquake and the issue of ongoing homelessness in Haiti.” (Monash Uni). This question lacks a clear task and just asks for a description of the connection between the events. ➔ A question that’s too broad can be difficult to answer within a given word and/or time limit. Not be too narrow. ➔ Eg. “What impact did the Haitian earthquake have on the rate of homelessness?” (Monash Uni) This question also asks for a description of impacts rather than an analysis and is too easy to answer. ➔ A question that’s too narrow will be too easy to answer, and will make developing a strong argument difficult. Require an analysis rather than a description of an issue, and analysis of data. Have relevant research material available, i.e., academic journals and refereed (peer-reviewed) journal articles. Allow the researcher to address a singular topic in depth rather than briefly addressing several topics. Have several potential answers. Be measurable/investigable. - It needs to be something you are able to answer. The reader must understand what you are trying to find out. Be relevant to the field ❖ Developing a research question from preliminary research (after having identified areas where research is taking place): 1. Narrow down the topic: 1. Identify subtopics/areas of debate, limited research or interest within the broader topic. 2. For each of these subtopics/areas, write down their potential value for your project and significance/contribution to scientific knowledge. 3. Identify the area with the strongest potential value. 2. Write the question: ➔ Keep in mind the purpose of the research when writing the question. ➔ Questions using ‘How’ and ‘Why’ are more useful than ‘What’ or ‘Describe’ questions, as they require analysis of an issue. ➔ It may be useful to write the question so the research is focused on a particular place and/or time period. ❖ Summary (Uni Melbourne): 1. If you have data: Identify a broader subject of interest arising from the data. If before data, identify a broader subject of interest. Eg. Cyberbullying 2. Research that topic to find what literature and data exists. 3. Narrow down your topic by asking “open-ended ‘how’ and ‘why’ questions. Eg. Why is cyberbullying becoming more prevalent? 4. Aim to get a specific measurable question: Eg. “What are the social factors leading to the increasing prevalence of cyberbullying in young Australians? It should be open-ended enough such that it can be answered by a ‘yes’ or ‘no’ or a number. 5. Form a purpose statement (why are you investigating x issue). 6. Revise the research question based on the purpose statement. (include causal factors and specific places/times). Formulate an initial scientific hypothesis based on the scientific research question: ❖ S cientific hypothesis: A formal prediction for a phenomenon. “It relates an independent variable to a dependent variable” in a testable causal relationship. ❖ Hypotheses are verifiable/falsifiable (testable) statements. ❖ A prediction of the consequence of changing the independent variable. ❖ “Are considered valuable even if proven false.” ❖ E valuate 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: Acronym Term Meaning In a test C Currency ow recent is the information? H - Check when the info was “Currency refers to whether the published. ideas being evaluated are part -Check if the information is too of the contemporary research out of date for the project. paradigm, rather than their -Check if it has been revised or dates of publication.” updated R Relevance Is the information related to the - Identify the intended audience. topic under investigation? Eg. student, public, researcher, etc. -”Is the level of information too basic or advanced.” -Is it in-depth or is it a summary? A Authority Who published the information? - Identify the author and find out if they are an expert in their field. -Have they published any other literature on this topic? -”Can you verify the author’s credential or the organisation they represent?” A Accuracy Is the information accurate and - Have other sources verified the reliable? claims? -”Is there a reference list citing their supporting evidence?” -”Do they outline the methods and data used in their analysis?” -How many citations does the paper have? -Are they published in a well-regarded refereed journal? P Purpose hat is the intention of the W - Identify the intention of the information? information; is it to “entertain, inform, educate or perhaps sell an idea or product?” Title Currency Relevance Authority Accuracy Purpose hemical C ublished 2021 P I wanted to test he author has T I found other Exposure-Induc (recent) and the developmental not published articles that also ed contributes new neurotoxicity of many articles - just used similar Developmental information. paracetamol to 2. methodology and Neurotoxicity in planarians. This There education quantification of article discusses was finished behaviour Head-Regenerat how they are a recently. (cognitive function ingSchmidtea good model for score) which both mediterranea developmental eoncluded that neurotoxicity and ethanol induces a provides a clear delay in behaviour method in how reacquisition. they tested developmental It is published an a neurotoxicity. The journal - meaning articles is in depth it was peer and will be helpful reviewed in the ‘Toxicological development of Sciences.’ procedures/metho ds in my experiment. -Assessing the current state of the theory, concept, issue or problem being considered: A ssess 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? C onduct a detailed literature review to support the validity, significance and appropriateness of the scientific research question: ❖ L iterature review: The section of the paper where there’s “extensive reference to related research in your field; it is where connections are made between the source texts that you draw on, and where you position yourself and your own work amongst thee sources.” It “includes background information enabling the reader to understand they key areas involved.” ❖ A literature review involves the evaluation of sources. - Journal articles are preferred since they are peer-reviewed. ❖ Questions to ask when evaluating a source: Who are the researchers and what are their reputations and disciplines? What perspectives did they have, and could this have been influenced by bias? “Do the findings support the research?” What were they aiming to prove/show/find and was this achieved? What is the significance of the research? Are there any limitations to the methodology or conclusions made by the researchers? Is it relevant to your work? “How does the research demonstrate an evolution of ideas and paradigms?” Is there research that supports or refutes their work? ❖ The notes about each source could be organised in a few different ways. This should depend on the research question and aims of the study. Topically/thematically. Based on the author's perspective on the issue. Chronologically. Based on the methods used. ❖ Elements in writing a literature review: 1. Description/summary: ➔ Describe what happened. ➔ What methods did the author use? ➔ What were their findings and what did they discuss? 2. Critical/interpretive: ➔ “Analyses, explains and interprets the information.” ➔ “Synthesises information to develop a point of view.” ➔ “Asks and answers questions.” ❖ Critiquing literature should take the form: Description of paper → critique (eg. this was limited because…) → Offering a solution → Positive critique (i.e. in this ‘pivotal/milestone/significant/etc study…) ❖ Literature review checklist (Uni Melbourne): Read and evaluate sources. Use clear Structure. Report and describe research. Interpret research. Critique research. Present solutions. Write on topic. Use your voice (form a point of view). Formulate a final scientific hypothesis based on the scientific research question: ❖ Null and alternate hypotheses. Develop the rationale and possible outcomes for the chosen scientific research: ❖ “ Justification for choosing the topic of study” ❖ “Explains why the research was performed.” Develop a detailed plan to investigate the scientific hypothesis including: -The overall strategy: -Methodology: ❖ Describes the procedure a researcher takes to address the objectives. ❖ “It involves the researcher’s justification behind using the method. ❖ Process of writing effective methodology: 1. “Introduce the overall approach to investigate → qualitative or quantitative? 2. “Show how the approach fits the overall design → your methods should address the problem.” 3. “Describe the specific methods of data collection to use → 1st hand or 2nd hand.” 4. “Analysis of results → statistical analysis?” 5. Provide background + rationale for unfamiliar readers.” 6. “Justification for subject selection + sampling procedure → Variables, repeats, specialist equipment and why?” 7. “Describe potential limitations → practical limitations, errors, etc.” 8. “Show that you pursuing this method outweighs the risk of these problems.” -Data analysis: -Representation and communication of the scientific research: -Timelines: -Benchmarks: ❖ “They are dates and targets you set yourself in your project that goes with the timeline.” C ritically analyse the scientific research plan to refine and make appropriate amendments: Employ accepted referencing protocols, for example: ❖ [brackets] indicate where a specific piece of information must be included in the reference. ❖ Example references are methods to reference a journal article. -APA: ❖ ased off of APA 7 - American Psychological Association 7th edition. B ❖ In-text citations: ([Author surname}, [year of publication]) ❖ Direct quote (word for word): ([Author surname], [year of publication], [page number/s]) ❖ When you have multiple works from one author in the same year add a letter to the end of the year. Eg. ([Authors surname], [year of publication]a) ❖ If the article references another work, referencethe article you have,but try to access theoriginal source.Example in text reference:[Authors surname]‘study’ ([year of publication]as cited in [Authors surname] [year of publication]). ❖ Use ‘and’ for sentences and ‘&’ for parentheses. I.e. [Author 1 surname] and [author 2 surname] ([year of publication]) ([Author 1 surname] & [Author 2 surname], [year of publication]) ❖ Reference list: Alphabetical order; don’t use numbers or bullet points. List up to 20 authors in a reference; list the first 19, thhen add an elipses and list the last authors name. Example full reference: [Author surname], [Author first initial]. ([year of publication]). [Title of resource]. [Journal title], [Volume number]([issue number]), [page number/s]. [doi] -Havard: ❖ ([Author surname] [year of publication]) OR [Authors surname] ([Year of publication]) ❖ When including a direct quote: ([Author surname] [year of publication]:[page number]) ❖ Up to 2 authors cited in text. For 3+ use ‘et al.’. ❖ If an author has done multiple works in one year, include a letter after the year number to indicate the order. I.e. ([Author surname] [year of publication]a) fir the first and ([Author surname] [year of publication]b) for the second. ❖ If the article references another work, referencethe article you have,but try to access theoriginal source.Example in text reference:[Authors surname]‘study’ ([year of publication]cited in [Authors surname] [year of publication]:[page number/s]). ❖ Reference list: Include DOI Alphabetical order Example full reference: [Author 1 surname] [Author 1 first initial], [Author 2 surname] [Author 2 first initial] and [Author 3 surname] [Author 3 first initial] ([year of publication]) ‘[resource title]’, [journal title], [volume number]([issue number]):[page number/s], [doi] -MLA: ❖ M LA (Modern language association) is widely used when writing about language and literature (in humanities). ❖ In-text citation for one author: ([Author’s surname] [page number]) ❖ In-text citation for two authors: ([Author 1 surname ‘and’ Author 2 surname] [page number]) ❖ In-text citation for 3+ authors: ([Author 1 surname ‘et al.’] [page number]) ❖ Multiple citations in parentheses: ([Author surname] [page number] ; [author surname] [page number]) ❖ Multiple works by one author: ([Author surname], [Title] [page number]) ❖ For authors with the same surname use their first initial, or full name if they share a surname and initials. ❖ If the article references another work, referencethe article you have,but try to access theoriginal source.Example in text reference:[Authors surname]‘study’ (qtd. In[Authors surname] [volume number]). ❖ ❖ Cited list: Alphabetical order. Use the authors full names (‘et al.’ for 3+ authors), invert the first author’s name i.e. ([author 1 surname], [author 1 first name] and [author 2 full name]) If the source is from a library database “include the database name in italics before” DOI (digital object identifier) or URL. Use DOI if available, but if not use the url (exclude ‘http://’ or ‘https://’ Example full cited list reference: [Author 1 surname] , [author 1 first name], et al. “[resource title]”[journal name], [Volume number],[issue number], [date of publication], pp.[page number/s].[publisher name], doi: [doi] OR[url]. Methodology and Data Collection Inquiry Question: How is an appropriate methodology developed to collect valid and reliable data? A ssess and evaluate the uncertainty in experimental evidence, including but not limited to: -Systematic errors: ❖ Systematic errors: “Deviations from the true value by a constant amount.” ❖ Affects the accuracy of an experiment. ❖ Can be reduced by: Using more precise/accurate equipment. Calibration/’zeroing’ of instruments: Done by “comparing the readings on the instrument with those given by a reference instrument or calibrator.” Correctly reading measurements. ❖ Examples: Calibration error: “the difference between the actualvalue of a physical quantity and the value measured by asensor. Parallax error: “The error that occurs due to incorrect positioning of the eyes while taking a reading on a measuring scale.” “The apparent displacement or the difference in apparent direction of an object as seen from two different points not on a straight line with the object.” Limits of measurement eg. ‘Half least scale reading’: “For analog instruments with graduating scales usually your uncertainty is half of the smallest increment you can measure.” ➔ Example: If you measure an object to be approximately 10cm using a ruler (the object tip is closest to the 100mm mark) then your length is given by (100±0.5mm). This is because mm is the smallest scale on the ruler. -Random errors: ❖ Random errors: “Cause deviations from true values by varying amounts.” ❖ Affects the precision and reliability of measurements. ❖ “Random error occurs due to chance.” They may be caused by slight fluctuations in: The environment. The way a measurement is read (i.e. misreading). ❖ Can be reduced by: Repeating measurements and calculating an average. This reduces the effect of extreme values (too high/low). This average “is more representative of the measured value.” D rawing a graph with a line of best fit: this accommodates “as much of the data as possible by cutting in between the set of data points.” More weighting is given to the most similar values. “This reduces the effects of random error and increases reliability.” Assess and evaluate the use of errors in: ❖ Types of errors: Measurement errors: “The difference between the measured quantity and its true or reference quantity value.” Instrumental: “When an instrument itself is flawed and provides inaccurate readings.” Observational: “When the observer incorrectly reads a measurement.” Environmental: “When problems in the lab’s surroundings lead to inaccurate results.” Theoretical: “When experimental procedures, a model system or equations for instance, create inaccurate results.” -Mathematical calculations involving degrees of uncertainty: ❖ Absolute error = |measured value - true value| ❖ Relative error = (measured value - true value)/true value x100% -Graphical representations from curves of best fit: ❖ Uncertainty can be graphically represented as an error bar. ❖ G iven two lines m1 and m2, that represent the minimum and maximum possible gradients (respectively) that lie through the error bars of all points: Percentage uncertainty = (m1-m2)/m x 100% Gradient of line of best fit can also be written as m±a, where a is the average distance from m (of m1 and m2) ❖ When uncertainty exists for both values (x and y axis), both vertical and horizontal error bars are represented (forming an error rectangle) Compare quantitative and qualitative research methods, including but not limited to: ❖ hat type of errors exist in quantitative vs qualitative research and how can they be minimised? W ❖ Qualitative: Bias/ (observational?) ❖ Quantitative: Systematic errors. I.e. parallax, calibration, limits of measurement. ❖ Advantages/disadvantages: ❖ Types of data: ❖ Comparison table: -Design of method: ❖ Qualitative research: Aims to: ➔ Explain phenomena through collection of narrative data. ➔ Understand how others experience the world and events in their lives. ➔ Explain human behaviour. Generally uses inductive reasoning Method design is subjective and holistic. Inductive reasoning: “Making generalised conclusions based off of specific scenarios.” The hypothesis isn't fixed and can evolve as the study progresses. ❖ Quantitative research: Aims to predict or explain phenomena through collection of numerical data and deductive reasoning. Deductive reasoning: “A logical approach where you progress from general ideas to specific conclusions.” A specific, clear, testable hypothesis is set prior to conducting the study. The research setting is controlled. -Gathering of data: ❖ Qualitative research: Sample types: ➔ Quota: ➔ onvenience C ➔ Purposive ➔ Self-reflection ➔ Snowball: The researcher recruits an individual, who goes and recruits new individuals who fit the define criteria of the researcher. Prone to bias Narrative/survey. Narrative data: Narrative data refers to “stories, accounts, or personal experiences shared by individuals.” Forms of data collection: ➔ Observations. ➔ Unstructured, informal focus groups. ➔ Extensive field notes are made. ❖ Quantitative research: Sampling is random. Measurement is standardised and numerical. Necessary variables are controlled. Forms of data collection: ➔ Semi-structured formal focus groups ➔ Interviews -Analysis of data: ❖ Qualitative research: The data is descriptive and observations/comments are used to come to a conclusion. Conclusions are generalisations and are reviewed/changed on an ongoing basis. Narrative analysis: Narrative analysis is used to understand how participants “construct story and narrative from their own personal experience.” Participant interprets their own lives in narrative → Researcher interprets construction of narrative. ❖ Quantitative research: Statistical analysis of numerical data is performed. “Conclusions and generalisations formulated at end of study, stated with predetermined degree of uncertainty.” Investigate the various methods that can be used to obtain large data sets, for example: -Remote sensing: ❖ Remote sensing: “Obtaining information about an area or phenomenon through a device that does not touch the area of phenomenon under study.” ❖ Passive remote sensors: Detects energy emitted/reflected from another object. Eg. Radiometers, spectrometers. ❖ Active remote sensors: Provide electromagnetic radiation to illuminate the object they are observing, and detect radiation that’s reflected/backscattered from the object. Eg. Radar. ❖ Allows real-time observations over large areas. ❖ Example applications: “Measuring ocean temperatures.” “Tracking cyclones and bushfires.” “Monitoring volume levels in dams or large tanks.” “ Measuring important parameters (eg temperature) at different points of a manufacturing process.” -Streamed data: ❖ Streaming data: “The continuous flow of data generated by various sources.” ❖ Streamed data can be time-sensitive and is continuous. ❖ Allows data recording in large amounts and in short time periods. ❖ “Data can be directly streamed and monitored from sensors via bluetooth/wi-fi.” ❖ Example applications: Measuring climatic data. “Tracking wildlife using GPS sensors.” ropose a suitable method to gather relevant data, including large data set(s), if P appropriate, applicable to the scientific hypothesis: ❖ Data gathering may be done by: “Observing the natural world.” “Experimenting in a laboratory.” “Running a model.” ❖ Method must be repeatable and include specific quantities and equipment. ❖ Processing Data for Analysis 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: ❖ T o be used the data needs to be processed (stored, sorted, filtered). ❖ “It is necessary to process the data in real time to reduce the amount of data that is placed in long term storage.” ❖ Methods of data processing: Manual. Mechanical Electronic - using excel. ❖ Types of processed data: “Plain text file, table/spreadsheet, chart + graphs, maps.” C onduct 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): ❖ The world’s largest and most powerful particle accelerator. ❖ -Kepler Telescope: ❖ A space telescope launched by NASA “to discover Earth-size planets orbiting other stars.” Advantages Disadvantages Discovered over 2600 planets T he photometer (main instrument of the telescope) is sensitive to temperature. -Human Genome Project: ❖ An international project in 1990 that aimed to “sequence all base pairs in the human genome to identify a complete set of DNA in the human body.” ❖ Made public 2003 C onduct 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: ❖ Open data repositories “provide free, immediate + permanent access to research to anyone.” Advantages Disadvantages ❖ Innovation: Allows the development of new initiatives. ❖ If it’s an experiment involving humans - it could involve ❖ The accessibility of the data leads to “increased publicising confidential info. community engagement, improved efficiency and reduced ❖ Analysis software may not be readily available due to the cost,” and “encourages progress and innovation.” cost. ❖ Secondary research can be performed by combining ❖ Data may not be secure. available data sets and without gathering new data. ❖ Unaware of the competence of those analysing the data - ❖ incorrect analysis may lead to incorrect conclusions. To do: ❖ W hen does the peer review process fail - examples. ❖ Two planarians were cut - one into two pieces and the other into free. They are in one dish whilst the other dish contains 3 planarians. ❖ Peer review: Pros: ➔ Allows selection of best articles to be published in a journal and best grant application for funding. ➔ Allows the assessment of the quality of a paper before it is published. Cons: ➔ Bad at detecting fraud and defects in scientific papers. ➔ Subjective process, and is prone to bias. ➔ expensive Module 3: The Data, Evidence and Decisions Patterns and Trends Inquiry Question: What tools are used to describe the patterns and trends in data? Analyse and determine the differences between data and evidence: ❖ M odern scientific research involves collecting data from making observations and measurements. This contains a degree of uncertainty/error, which can be quantified and characterised. Data contains patterns and trends which can be analysed to derive meaning. ❖ Scientific Investigation: Refers to “How scientists use the scientific method to collect the data and evidence that they plan to analyse.” Uses “empirical data, verifiable evidence, and logical reasoning.” ❖ Empirical data: “Facts, numbers, and statistics measured in the real world and collected together for analysis.” ❖ ❖ Data: “Factual information such as number, percentages, statistics.” Data is raw information Quantitative data: Numerical data Qualitative data: Consists of “Personal accounts and descriptions from large numbers of people.” A dataset is an organised collection of data. ❖ Evidence: Relevant data that furnishes proof and supports a conclusion. Involves using data to prove/disprove a particular point. Evidence shows whether a hypothesis is true. “Evidence should always be verifiable” The same results should be achieved by collecting evidence again. D escribe the difference between qualitative and quantitative data sets, and methods used for statistical analysis, including but not limited to: ❖ Qualitative data analysis may involve typology: Typology: “A system used for putting things into groups according to how they are similar.” It involves systematic classification according to common characteristics. Groups may be made based on sensory information, logic, etc. ❖ -Content and thematic analysis: ❖ Content analysis: Used to “determine the presence of certain words, concepts, themes, phrases, characters, or sentences within texts.” This presence is quantified. Texts may be any occurrence of communicative language, eg. books, essays, discussions, interviews newspaper headlines/articles, speeches, historical documents, “conversations, advertising, theatre, informal conversation, films, photos, websites”, etc. To conduct content analysis, the text is coded into categories, i.e. “word, word sense, phrase sentence, or theme” and examined. The results of content analysis allow inferences on the messages within the text and the “culture and time of which these are a part.” Content analysis quantifies the content of the data, and is objective - it focuses on describing/analysing surface-level characteristics. ❖ Coding: “Systematically categorising excerpts in your qualitative data in order to find themes and patterns.” At this stage, all qualitative data has been collected, whether visual, written or recorded (eg. interview). A code “symbolically assigns a summative or evocative attribute for a portion of qualitative data.” Coding involves looking for patterns/similarities/relationships in qualitative data, “to explain why things happen.” You may create a single code to describe a large text. Codes are specific to your research question. Eg. It could involve coding for frequency in statements/behaviour, or sequence (before/after implies causation) Qualitative research “can help you discover a causal chain of events.” Inductive coding: ➔ “You don’t start with preconceived notions of what the codes should be, but allow the narrative or theory to emerge from the raw data itself.” ➔ This is used for exploratory research. ➔ The researcher goes through the texts several times and builds theory based on the patterns that emerge (grounded theory). ➔ Codes are generated based on emergent themes. Deductive coding: ➔ Important questions are set before analysis (these questions can be codes). Large parts of data may be disregarded in order to answer these questions. ➔ For example, emotion could be set as a code. The researcher would then go into the text and quantify the presence of this code. ➔ The theory has been created before coding, and the researcher wants to clarify the answer to their research question. ➔ These predetermined codes are related to the research question. ❖ Content analysis aims to: Provide an insight into the trends and relationships within a text. Describe attitudinal/behavioural responses to communications. To present characteristics and aspects of content clearly and effectively. “To determine psychological or emotional state of persons or groups.” Identify intentions, focus or communication trends of an individual/group/institution. Construct an argument to answer a research question. ❖ Process of content analysis: 1. Analysis: Read transcripts and texts/recordings/images. Classify elements of discussion in the texts. Create codes (categories) and areas of discussion 2. Interpretation: Review the codes, and create subcategories. Interpret and code the data, by organising it into the categories and subcategories 3. Synthesis: Interrelate the interpretations of data within sub and major categories. Develop priority discussion areas and report format. 4. Write a detailed report, developing conclusions from the interpretation of data. Write recommendations from these conclusions. ❖ Thematic analysis: A form of content analysis, that involves identifying and interpreting patterns and themes in data to understand underlying concepts and ideas present. Thematic analysis uses inductive coding, whilst content analysis uses deductive coding. Process: 1. Familiarise yourself with the data, recording any noticeable patterns. 2. Generate initial codes, and label notable features of data with these codes. 3. Identify patterns within the code, and generate themes. Encompass multiple codes in one theme. 4. Review the data, and assess if the themes accurately represent the data. 5. Apply concise names to the themes 6. Interpret and report the data, paying attention to the frequency and presence of the themes. ❖ Advantages of thematic analysis: Provides a lot of flexibility in applying theories. Allows the data to shape the conclusions - categories evolve from data. Helps the interpretation of themes that are supported by data. ❖ Disadvantages of thematic analysis: U nreliable - data may be interpreted in many ways by different researchers. Subjective in nature. It becomes difficult to narrow a focus on the data, as their are so many themes that could arise. “Thematic analysis might miss variations in data.” ❖ -Descriptive statistics: ❖ “A descriptive statistic is a summary statistic that quantitatively describes or summarises features from a collection of information.” ❖ Descriptive statistics is the process of using/analysing this information S elect 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: -Spreadsheets: -Graphical representations: -Models (physical, computational and/or mathematical): -Digital technologies: A ssess the relevance, accuracy and validity of the data and determine error, uncertainty and comment on its limitations: ❖ M easurement errors are differences between the measured quantity value and its reference quantity value. ❖ “Scientific error is not a mistake in making the measurements.” ❖ Systematic errors, such as calibration, parallax and limits of measurement, i.e. half least scale affect accuracy, but not reliability of measurements. Repeating measurements doesnt improve accuracy. ❖ Random errors: Occur due to chance Slight fluctuations in an instrument or environmental conditions can cause random errors Variations from the true value by random amounts. Random errors affect precision and reliability. Repeating measurements can reduce random errors of measurement. ❖ Closer measured value to true value → lower error. (By mathematical definition). ❖ Lower error → greater accuracy of measurement. Evaluate the limitations of data analysis and interpretation: Statistics in Scientific Research Inquiry Question: How does statistical analysis assist in finding meaning in the trends or patterns in datasets? Apply appropriate descriptive statistics to a data set(s), including but not limited to: -Mean: -Median: -Standard deviation: A pply appropriate performance measures to the statistical analysis of quantitative data set(s) obtained from conducting a relevant practical investigation, including but not limited to: ❖ Reliability: Internal: Consistency of repeated measurements within an experiment. This is synonymous with precision. External: Consistency of measurements over a number of independent experiments. I.e. have other researchers, who’ve made the same measurements obtained the same results? -Error -Accuracy: ❖ Accuracy: Closeness of a measurement to its true value. ❖ Determining the accuracy of measurement requires prior knowledge of the true value/reference data. ❖ -Precision: ❖ Precision: “The extent to which repeated measurements, made under identical conditions, agree with each other.” I.e. the closeness of measurements to each other. ❖ Precision is examined using range, variance, standard deviation and standard error. ❖ -Bias: ❖ B ias: “The tendency of a statistic to overestimate or underestimate a parameter.” Trends or deviations from the truth “in data collection, analysis, interpretation and publication.” Bias can occur during sampling. ❖ Bias can lead to false, misleading conclusions. It is unethical to conduct biased research. ❖ Potential sources of bias should be minimised to avoid deviations from the truth. ❖ Sampling variability: “Tendency of a statistic to not match the population exactly.” ❖ Measur ❖ Selection bias: “Occurs when individuals or groups in a study differ systematically from the population of interest leading to a systematic error in an association or outcome.” ❖ Publication bias: Scientific journals are more likely to publish studies with positive findings than negative findings. This results in other researchers waiting their time and financial resources by re-running completed unpublished experiments with negative findings. ❖ Funding bias results from being influenced by the funding company to produce results that are in their favour. -Data cleansing: ❖ Involves removing data that’s incorrect, irrelevant, duplicated or improperly formatted. This is done to prepare it for analysis. ❖ Duplicate observations can bias results ❖ Small, incomplete or irrepresentative data can skew results. ❖ Poor quality data is harmful “Data needs to be accurate and precise and collected by someone trained to do so.” ❖ P rocess of data cleansing: 1. Remove unwanted observations, such as duplicate and irrelevant data. 2. Fix structural errors. I.e. typos or inconsistent capitalisation 3. Filter unwanted outliers. Not all outliers should be removed, you must have a legitimate reason. 4. Handle missing data, by labelling it as ‘missing’. Deleting observations with missing data, or imputing data is inappropriate as it either excludes information or reinforces preexisting trends. 5. A pply appropriate performance measures to the statistical analysis of a data set(s) relevant to the Scientific Research Project: A pply appropriate statistical tests of confidence to a data set(s), including but not limited to: ❖ N ull hypothesis (H0): An observed difference reflectschance variation. I.e. There is no difference between groups. ❖ Alternative hypothesis (HA): There is a differencebetween groups. “Observed difference is real." ❖ Test statistic: Measures the difference between the data, and what is expected (by the null hypothesis). Z-statistic “converts the observed value to standard units, on the basis of the null hypothesis.” Z-stat describes how many standard deviations away an observed value is from its expected value. ❖ Hypothesis test (Test of significance): “Allows analysis of whether an observed difference is real or just a chance variation.” ❖ “The significance level is the probability of rejecting the null hypothesis when it is true.” ❖ Lower significance level → means stronger evidence is required to reject the null hypothesis. ❖ P≤0.05 → reject the null hypothesis, where the level of significance is 0.05. → effect is statistically significant. ❖ P-value (probability value): Measures the extremeness of the data, i.e. the probability of obtaining a sample statistic with a value as or more extreme than the one determined from sample data (what was actually observed), if the null hypothesis is true. “The p-value is the probability we would get the sample we have or something more extreme if the null hypothesis were true.” p𝛂: There is insufficient evidence to reject the null. ❖ Type I error: Incorrectly rejecting null ❖ Type II error: Incorrectly failing to reject null. ❖ Since your decision to reject/fail to reject the null is based on a sample, there’s a possibility of error in analysis. ❖ You may have to discuss the costs associated with type I/type II errors. ❖ If a type I error would be more serious, for example, pharmaceuticals, the significance level should be smaller. ❖ A NOVA: Analysis of Variance Similar to a t-test, except it determines whether 3+ populations are statistically significant to each other. ANOVA utilises the F statistic. ANOVA can be used with 2 groups, though this will provide the same results as a t-test. -Student’s t-test: ❖ Two-tailed tests are used when the sample group could be lower or higher than the control/reference group. ❖ Critical value is what a test statistic must exceed for the null hypothesis to be rejected. I.e. if |t| > t critical, reject the null. ❖ Used for small samples, i.e. 30. ❖ Assumptions: Random sampling. Normal distribution/normality. Equality of variance (homoscedasticity) ❖ Compares mean values of groups. ❖ Gives a t statistic, which can generate a p value. ❖ One sample: Compares a mean with an expected population mean. ❖ Two samples: Compares means of two groups. ❖ Paired: Compares means of two data sets for the same group at different times. ❖ -Chi-squared test: ❖ Determines relationship between categorical variables. ❖ Assumptions: Categorical data. Independent groups - i.e. each data point can only be represented in one category. Data in cells should be frequencies, not percentages. ❖ Χ value allows calculation of p value. 2 ❖ Chi-squared goodness of fit: Determines whether the observed frequency is the same as the expected frequency. Uses one categorical variable - where a numerical variable is used as a count/frequency. The expected frequency is calculated by multiplying the frequency by the observed frequency. This is done for each group in the table. -F-test: ❖ Compares the variances within two groups of data. ❖ Allows us to determine if samples from populations have equal variances, and if new processes/treatments/tests reduce the variability of the current process - Compares the groups at different times. ❖ Produces a ratio of variances between groups ❖ Assumptions: Normality from both populations Populations are independent. ❖ A p-value can be determined using the F test, allowing us to make an inference about the variation. ❖ If F is close to one, evidence favours the null (population variances are almost equal) ❖ If F is greater than one, evidence is against null hypothesis → Population variances differ significantly ❖ There are two F critical values, one below and 1 above 1. If the F statistic is less than the lower one or greater than the higher one, reject the null hypothesis about equality of variance. A pply statistical tests that can determine correlation between two variables, including but not limited to: ❖ C orrelation: “Measures the strength of association between two variables and the direction of the relationship.” ❖ -Correlation coefficient: ❖ Pearson’s correlation coefficient, r, describes the degree of strength of a straight line relationship between two variables. ❖ r= -1: Perfect negative relationship ❖ r= 1 : perfect positive correlation ❖ r= 0 : No relationship ❖ Cannot be used for purely categorical data. ❖ R describes how much one variable tends to change when another does. ❖ “Quantifies the degree to which two variables are related.” ❖ USed when both variable are measured. ❖ Coefficient of determination (type of linear regression): Coefficient of determination is r2 (square of correlation) Measures variability in dependent variable values 0 < r2 < 1, where r2 = 1 indicates all points arein a straight line. Can be used to create a straight line of best fit. Linear regression is used when you have a dependent and independent variable. IT can be used to form a linear equation in terms of x. ❖ Describe the difference between correlation and causation: ❖ C ausal effect: A result that a change in one variable leads to (causes) a change in another variable. ❖ In order to establish a causal relationship, 5 criteria must be met: Association: They must have an observed proportional relationship (whether inversely or directly proportional). Time order: Change in the independent variable must occur before the change in the dependent variable. I.e. the cause must come before its effect. Nonspuriousness: The relationship can't be spurious, meaning the correlation between the variables can't be caused by a 3rd variable that's changing. Eg. There is a positive relationship between children’s shoe size and academic knowledge, but both of them increase due to age. Identification of a causal mechanism: A mechanism that relates the change in an independent variable to a change in the dependent variable must be known. C ontext: This is not 100% necessary but helps understand a causal relationship. Eg. In capitalistic societies like America, incentivising employees with higher pay for longer work will often be effective. In noncapitalistic societies, however, many people will just want the money to meet their basic needs. ❖ In an experiment, determining causality can be done by: Correlation coefficient (association) Variation in independent variable before assessment of change in the dependent variable (time order) Random assignment to 2+ comparison groups → establish nonspurriousness ❖ Explain the requirements to establish causation: U se available software to apply statistical tests appropriate to a large data set(s) to assist with the analysis of the data: Decisions from Data and Evidence Inquiry Question: How is evidence used to make decisions in the scientific research process? Assess the benefits of collective and individual decision-making: ❖ Individual: Benefits: ➔ The individual can make prompt decisions and implement them. Groups, however are dominated by several people and decisions require debate. Thus individual decision making saves time. ➔ Individuals are accountable for their own actions/decisions/performance. ➔ Decisions are more focused abd rational compared to a group. Disadvantages: ➔ Cannot collect as much information as a group can. ➔ A singular view and approach is applied t the research, while with groups a more holistic approach may be achieved. ➔ Doesnt take into account the itnerests of others in an organisation. ❖ Collective: Benefits: ➔ Generate more complete information and knowledge. ➔ Brings more input and diversity into decision process. ➔ Generate higher quality decisions/research due to multifaceted exploration. Disadvantages: ➔ Assembling a group can take time. ➔ Group decisions are time consuming, as it considers the views of more people. ➔ Pressure to conform to a view may exist. ➔ Group decision may be dominated by one or a couple members. ➔ There can be ambiguity in who is responsible., whilst in an individuals decision, its clear who’s responsible. A nalyse 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: d emonstrate the impact of new data on established scientific ideas, including but not limited to one of the following: -Gravitational waves on general relativity: ❖ Mechanisms of disease transmission and control: ❖ Advancements in genomic sequencing has provided nformation about pathogen transmission and evolution. ❖ Real time epidemiological tracking hhas revolutioned tracking infectious diseases. Eg. mobile health app, e health recordsm social media monitoring allow rapid outbreak detection. Time series analysis allows us to understand causes of trends/patterns overtime. Eg. time series forecasting. ❖ Modelling and predictive analysis of infectious idsease has allowed us to predict changes in infectious disease by looking at histroical patterns. This thus allows us to act accordingly to combat the disease spread. ❖ Understanding that asymptomatic individuals may still be carriers of the disease and infect others has allowed us to implement qurantine and lockdown measures during COVID, which hence reduce the impact of COVID. RAT tests allow for quick and easy infectious disease testing. ❖ Combining social science and epidemiology has allowed us to understand how human behaviour can affect infectious disease spread. Aspects of human behaviour has mtigated disease spread, i.e. raising awareness of COVID-19 incidence. ❖ Tracking of antibiotic resistance genes has helped us understand dynamics of resistance and develop strategies to minimise it. In bacteria this is done by genomic sequencing. ❖ Studies have produced data about vaccine and booster performance in diverse communities. This has allowed mass development of vaccines to be used in public health. ❖ Telemedicine, remote monitoring and digital contract tracing are useful in monitoring disease spread. -Prediction of natural disasters: -Effects of chemical pollutants on climate: Data Modelling Inquiry Question: How can data modelling help to process, frame and use knowledge from the analysis of data sets? E valuate data modelling techniques used in contemporary science associated with large data sets, including but not limited to: ❖ D ata models are abstract models that organise elements of data and standardises how they relate to each other
