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People in our society use the word “science” to refer to what is real and true.
Let’s examine more closely what science is…
Science refers to humanity’s systematic study of the natural world through observation and experimentation in order to gain knowledge (from Merriam-Webster dictionary).
To study the natural world, humans apply methods of observation and experimentation.
Humans also propose hypotheses about the world and create measurement systems from their observations in order create data they can use to test their hypotheses.
What is the natural world? The universe with all its contents and physical laws, including matter, energy, plus millions of species and life forms.
The word “nature” refers to the natural world that is not created and controlled by humans. Human beings are part of the natural world, but they do not control all the laws of physics, life, and universe.
What is human? A particular type of life form on earth, a unique species out of 30 million species*, with each human being existing as a unique individual.
Science does not exist without humans.
Observations occur through the experiences of our human senses, including sight, sound, touch, smell, and taste.
Over centuries, humans designed multiple ways to measure what they observed and experienced through their sensing capacities.
Human measurement systems are based on social agreements regarding distance/space/weight observations.
For example, a measure of warmth and cold is called “temperature” in English and can be measured using different scales and reference points (ex, Celsius, Fahrenheit, Kelvin). A Celsius measurement scale is based on the freezing point of water which is called zero. A Kelvin scale zero point is the coldest temperature possible (absolute zero) which corresponds to -273.15 C and -459.67 F.
Humans observe and then apply their human-designed measurement systems and devices to create quantitative, numeric, empirical data. Data can be collected over time and across varying conditions, in order to look for patterns. Data analysis using mathematics and statistics are important tools when looking for patterns in quantitative data.
Sometimes humans count the frequency of observing certain physical phenomena that are not easily measured quantitatively, such as seeing body language and facial expressions, hearing tone of voice, and tracking movements of life forms and objects in the natural world.
Besides creating quantitative measurement systems and analyzing numeric data using statistics, humans also created their own languages to communicate words and meanings unique to the human species. Over 5000 human languages have been created across the world.*
When humans use their languages to communicate in words, they create narrative stories about their direct experiences and observations. These stories attempt to describe or explain human observations, and often the narratives themselves become a source of data. When humans analyze the meanings and interpretations of their narratives, it is called a study of “hermeneutics”.
Any time humans are using their senses to observe, take notes in a written language and symbols, counting the frequency of phenomena they see, or telling narrative stories about their experiences, then they are engaging observational methods.
In everyday language, experimentation is often called “trial and error”.
Experimenting takes scientific studies a step further. In academic language, experiments require at least one factor or variable in a study to be controlled, so that the effects of that factor can be observed and measured under at least two different conditions.
Here is an example of a scientific experiment:
A new treatment for diabetes is tested by randomly assigning 300 people to three different experimental conditions: 100 get the new medicine, 100 get the old medicine, and 100 get no medicine (placebo). Scientists control the treatment condition given to each patient, and then measure the effects on each person’s health over months.
In the best designed experiments, the treatment conditions are double-blind, meaning no one involved in implementation or data collection (not the doctor administering the treatment nor the patient, nor the scientists and researchers involved) have knowledge of which treatment each person is receiving until the results are measured.
Experiments are often designed to test a specific hypothesis that considers a potential cause and its effects. In comparison, observational data can only measure association (e.g. assess the strength of the relationship between two factors, such as diabetes and sugar intake, as a correlational number) but cannot test for causation (e.g. does sugar cause diabetes?).
Scientists observe, measure, and design experiments in order to test hypotheses.
A hypothesis is a proposed idea to explain phenomena observed in the natural world. Often an experiment is designed to test a specific hypothesis, and then observed measurement data is collected to see if numeric data supports the proposed hypothesis or not.
Scientific methods are not purely objective or unbiased. Every human bias affects humanity’s scientific methods of studying their natural world. More than 40 biases in human perception and decision-making have already been identified in published research:
Besides cognitive biases, human social groups demonstrate many biases as well. Humans’ social biases include social conformity, tendencies toward group-think (also called herd/mob mentality), and blindly complying with authority figures (even in ways that cause harm to others).
Research on human motivations underlying their cognitive and social biases reveal desire for protection (survival/safety/security), to gain rewards, avoid punishments and pain, feel good about self (significance, worth, value), and avoid feeling helpless/powerless and afraid.
Systemic biases in science include
(1) the intentions and self-interests of the scientists, since they design the study and interpret its results and then communicate a story about the study using one or more human-created languages.
Ask why are they doing this research, who rewards them for the research, how are rewards determined (e.g. number and quality measures of publications, “newness” vs replications, funding), and who benefits from rewards received?
(2) the funding of the scientists directly and indirectly through their employer’s reward system.
Who funds the scientist, and who funds the institute or university or company that funds the scientist?
Yes this really matters and statistically skews the results reported! Many research studies show a skew (bias) of research results to support the self-interests of the funding organizations.
For example, in medicine: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1494677/
In state and federal government: https://ori.hhs.gov/education/products/ucla/chapter4/default.htm
“A conflict of interest in research exists when the individual has interests in the outcome of the research that may lead to a personal advantage and that might therefore, in actuality or appearance compromise the integrity of the research.” NAS, Integrity in Scientific Research