BIOL 391 Intro to Biological Research Fall 2024 Hypothesis PDF

9/17/24 Chapter 6 Philosophy of The Sky is Red Hypothesis. Experimentation Examining the Hypothe...

9/17/24 Chapter 6 Philosophy of The Sky is Red Hypothesis. Experimentation Examining the Hypothesis-Falsification Ch. 6-8, Glass (2014) Framework. BIOL 391 Intro to Biological Research Burman, Fall 2024 1 2 The Sky is Red Hypothesis: “The Sky is Red” Experimental System: – Defined as the set of things required to conduct your experiment. – Method to measure “red” accurately 1. “Redometer” – measures radiant light and is calibrated so that it is capable of two readouts: “red” and “not red”. If “red” is detected then a signal/flag is recorded. Failure to see the signal/flag will mean that the hypothesis has been falsified. 3 4 The Sky is Red – Expt. System The Sky is Red – Expt. System 2. Positive control – an object known to be Scientist points the redometer to the sky for red, like a red ruby. 4 hours from 10 am to 2 pm. 3. Negative control – any object that is “not – No “red” flag raised and a negative result is red”, such as an emerald. Any colour other recorded. than red can fulfill the requirement to – Scientist feels that the redometer maybe measure “not red”. insufficiently sensitive, and would need a longer time period to record a “red” signal. Scientist points the redometer to the sky at 9 Scientist performs the experiment for 24 hours, am for 1 hour. the length of a full day. – No flag raised and a negative result is recorded. – “red” signal detected (because the red light of dawn – Scientist feels that 1-hour time frame is too short. and sunset occurred during this time period). 5 6 1  9/17/24 The Sky is Red – Expt. System The Sky is Red – Analysis The experiment is declared to have Scientist performs a statistical analysis worked because a positive result has regarding the number of times “red” been achieved. was detected. Experiment is repeated everyday for 1 – 29 out of 30 month. – Red detection showed statistical – All except one day (when a cloudy day significance obscured the sunrise and sunset), a “red” Scientist concludes that the sky is red. was recorded. 7 8 Hypothesis Framework - problems Can introduce inadvertent experimental bias. – Hypothesis demands a particular subset of measurements that ignores the the entire relevant set of data, e.g., sky is red, or not red (ignoring the other colours). – Results in a limited and misleading picture of reality (i.e., incorrect, incomplete, or misleading conclusion). 9 10 Hypothesis Framework - problems NF-kB Pathway Creates a positive/negative data filter. –Causes the scientist to value the “red” result significantly more than the much more common “not red” data. –Ignoring the value of other experimental data/outcomes. –e.g., activation of the NF-kB Pathway 11 12 2  9/17/24 Activation of the NF-kB Pathway Activation of the NF-kB Pathway Hypothesis: Activation of the NF-kB The hypothesis as written does not demand pathway results in inflammation. experiments that will delineate the In a more complex biological system like tissue-specific responses or even a firm this, the data obtained may vary definition of inflammation. dramatically. This may lead the scientist to keep looking for – May depend on cell type used (activation of the a system that may produce “positive” data – NF-kB in the liver leads to inflammation; not in hence, an emphasis on “positive” data as the skeletal muscle). opposed to any “negative” data that can – Results may differ depending on the criteria falsify the hypothesis. used to define inflammation. 13 14 Activation of the NF-kB Pathway Hypothesis-falsification Framework Inflammation is often defined by looking for the Might create selective data markers of inflammation in a tissue. These markers interpretation and an inappropriately could have been a result of several activation pathways, not related to the the NF-kB Pathway. restricted measurement of a complex Triggering of inflammation in some cases maybe due system. to another pathway, but the quest for “positive” data may influence the inclusion of this result. Selective measurement can cause Thus, if the hypothesis is influencing the mislabeling of a failure to falsify readout, interpretation of what is acceptable data and what is not, the reliability of experiment is in along with a claim of verification, all due question, hence conclusions invalid. to incomplete data sampling. 15 16 Chapter 7 Critical Rationalism The Hypothesis as a Framework for Rationalism is the belief that your life should Scientific Projects. be based on reason and logic, rather than emotions or religious beliefs. Is Critical Rationalism Critical Enough? 1. Reason above experience in knowledge acquisition. 2. Reason can justify our beliefs, claims, and theories. 3. It asserts that it is possible to obtain certain, unquestionable, foundational knowledge by reason. Critical rationalism rejects all of these. Critical Rationalism has been referred to, by Popper, as the theory of falsification. 17 18 3  9/17/24 Hypothesis-falsification Framework Unavoidable in practice – Framework is often required by funding agencies, journals, and other institutions. Therefore, if unavoidable, we should know how to avoid bias in experimental design and interpretation. 19 20 1. Grammatical Structure of Hypothesis and Conclusion The hypothesis has the same grammatical structure as the conclusion, and this similarity may induce an expectation of a particular result. The hypothesis, as well as the conclusion, always takes the form of a declarative statement. A hypothesis is required to be falsified, it must take this declarative form, which is like the way a prediction or postulate is commonly phrased. 21 22 “The Sky is Red” Example Bias Hypothesis: The sky is red. The identical phrasing and structure of both Expt: Test whether one can falsify that the the hypothesis and conclusion creates the sky is red, using a system to test “red” versus danger of confusing the hypothesis as an “not red”. unproven premise with the hypothesis as Framework-appropriate conclusion: There the actual expected outcome and further was a failure to falsify the hypothesis that confusing these with the actual result of the sky is red (because red was measured). the experiment. Common, actual conclusion made based on the experiment: The sky is red. 23 24 4  9/17/24 Leading Bias May not appear to be a “bias” to the scientist since the hypothesis creates the expectation of a particular result because it is identical in structure to a conclusion. Leading premise bias conclusion. 25 26 2. Positive Data Bias Experiments framed by hypothesis establish the idea of “positive” data versus “negative” data. Positive data are consistent with the hypothesis and negative data falsify the hypothesis. This creates a potential bias to amass positive data, owing to the desire to avoid falsification. “Once a man’s understanding has settled on something… it draws everything else also to support and agree with it.” Francis Bacon 27 28 e.g., “Caffeine increases blood e.g., “Caffeine increases blood pressure”. pressure” Demands the measurement of increased blood He may accept very small changes in bp as pressure (bp; “positive” data) – establishes a “positive” data – even if these small increases data filter for bp increases and ignoring other are not physiologically relevant. data, like decrease or no change in bp. If only “negative” data is recorded, the scientist The “caffeine increases blood pressure” may be tempted to change experimental hypothesis influences scientist to look for an conditions, like increasing caffeine dosage, to increase in bp (a decreased bp measurement is obtain “positive” data. required to falsify). The hypothesis establishes a A better approach would be to ask the question dysfunctional positive/negative binary, – “What is the effect of caffeine on blood because a “confirmation” result is only recorded pressure? – this approach is more inductive. when increased bp is measured. 29 30 5  9/17/24 e.g., “activation of enzyme X causes e.g., “Caffeine causes cancer” colon cancer” Epidemiological survey to determine if caffeine Negative results obtained in colon cells. consumption results in a higher incidence of cancer in caffeine vs no caffeine population. Positive results obtained in fibroblast cell line (3T3 cells). Even if a statistical difference is found in only one type of cancer (let’s say in 100 different Hypothesis must be aligned with an cancers), that cancer will recorded as “positive”. appropriate experimental system and not The scientist might say that the hypothesis is just one that produces a “positive” result. not falsified, hence claiming verification. Inappropriate conclusions are routinely made from such surveys. 31 32 3. Failing to Falsify (Confirmation) Bias. The scientist is not rewarded by falsifying a hypothesis but is rewarded only by failing to falsify, which is then labeled confirmation. Confirmation bias is the tendency to interpret new evidence as confirmation of one's existing beliefs or theories. 33 34 Only positive data! Previously, we see that there may be incentives to to keep changing things until a positive result is achieved (i.e., chasing “positive” data). Motivating factors for this phenomenon: 1. Scientists are rewarded for confirming, or failing to falsify, a hypothesis. 2. High-profile journals generally do not publish “negative” data. 3. Funding bodies also like positive data. 35 36 6  9/17/24 “Positive” Bias Questions as experimental frameworks. Bias towards publishing positive results in orthopedic and general surgery: a patient safety Biases induced by the hypothesis issue? falsification framework, with a love for Erik A Hasenboehler, Imran K Choudhry, Justin T Newman, Wade R Smith, Bruce H Ziran and Philip F Stahel verification and positive results, should be Patient Safety in Surgery 2007, 1:4 Research articles reporting positive findings in the fields of avoided. orthopedic and general surgery appear to be represented at a considerably higher prevalence in the peer-reviewed Using questions as experimental literature, compared to published studies on negative or frameworks could avoid these pitfalls. neutral data. This "publication bias" may alter the balance of the available evidence-based literature and may affect patient safety in surgery by depriving important information from unpublished negative studies. 37 38 Chapter 8 Scientific Settings in Which a Hypothesis- Falsification Framework is Not Feasible. – e.g., very large biological research projects, e.g., Human Genome Project, were launched without a preexisting hypothesis in place. – Or, any “systems biology” approach like: Transcriptomes Proteomes 39 40 Human Genome Project 41 42 7  9/17/24 Dr. Venter Shotgun Sequencing Inventor of “Shotgun Sequencing”. Feeling a need to offer a hypothesis when testifying before the US Congress, he stated “It is our hypothesis that this approach will be successful”. 43 44 Cost of Human Genome Project In 1990, the US Congress established funding for the Human Genome Project and set a target completion date of 2005. Although estimates suggested that the project would cost a total of $3 billion over this period, the project ended up costing less than expected, about $2.7 billion dollars. Additionally, the project was completed more than two years ahead of schedule. It is also important to consider that the Human Genome Project will likely pay for itself many times over if one considers that genome-based research will play an important role in seeding biotechnology and drug development industries, not to mention improvements in human health. 45 46 Human Genome Project A hypothesis was neither necessary nor would it have been especially useful in conducting the largest and most labor-intensive biological project in human history – yet extremely valuable information was gained by sequencing the genome. The formation of a useful hypothesis requires prior knowledge of the system – hence, “What is the sequence of the human genome?” generated more specific questions/hypotheses concerning the function of the identified genes. – Understanding genomes, search for cures to human diseases, …... 47 48 8  9/17/24 49 50 Predictions from the human genome What the next 50 years of medical science sequence look like post Human Genome Sequence? The data-gathering event was necessary to Having the essentially complete sequence of the formulate new reasonable hypothesis human genome is like having all the pages of a based on the obtained sequence. manual needed to make the human body. – Predict the function of a gene by identifying The challenge to researchers and scientists now is to sequence similarities to genes with known function. determine how to read the contents of all these pages and then understand how the parts work – Subsequent experiments could lead to experiments based on the hypothesis falsification framework together and to discover the genetic basis for when the problem is narrow and focused. health and the pathology of human disease. The human genome sequence can also In this respect, genome-based research will eventually enable medical science to develop highly induce many things about its sequence, effective diagnostic tools, to better understand the and subsequent experiments can be tested by health needs of people based on their the inductive method. individual genetic make-ups, and to design new and highly effective treatments for disease. 51 52 What the next 50 years of medical science look like post Human Genome Sequence? Individualized analysis based on each person's genome will lead to a very powerful form of preventive medicine. – risks of future illness based on DNA analysis. Physicians, nurses, genetic counselors and other health-care professionals will be able to work with individuals to focus efforts on the things that are most likely to maintain health for a particular individual. – diet or lifestyle changes, or medical surveillance – personalized aspect for healthful living 53 54 9  9/17/24 What the next 50 years of medical science look like post Human Genome Sequence? Then, through our understanding at the molecular level of how things like diabetes or heart disease or schizophrenia come about, we should see a whole new generation of interventions, many of which will be drugs that are much more effective and precise than those available today. 55 56 Pros and Cons: 57 58 10