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Scientific uncertainty is a quantitative measurement of variability in the data. In other words, uncertainty in science refers to the idea that all data have a range of expected values as opposed to a precise point value. This uncertainty can be categorized in two ways: accuracy and precision.
- Strength of Results
- Biological Significance
- Not So Relevant
- Act Like An Expert
Stats are key to good research – they help researchers determine whether the results observed are strong enough to be due to an important scientific phenomenon. As a research student I would always look for the magic number which indicates statistically significant differences in my experiments: most people agree this number to be 0.05 (you may see...
Biological significance addresses the question of whether the statistical difference actually means anything in terms of a real outcome, like a disease. Can the result explain how the disease is caused? Does it provide a new avenue to treat the disease? Basically, is it relevant? A recent paperpublished in the journal Leukaemia will help explain my...
Another result from the same paper (Figure 3a if you want to click throughto the data) shows a statistically significant difference in a sub-type of natural killer cells (called adaptive natural killer cells). But is this difference biologically relevant? At this stage there is little evidence of a role for adaptive natural killer cells in the cont...
So how do you pick if the statistical differences have biological value? Being a highly trained expert in the field certainly helps. Another way to determine if the findings in a paper have biological relevance is to look for other papers that show similar results. If a result is “real” it should be found by other scientists who will build on it an...
- Yazad Irani
- Anomaly: An anomaly is an observation that differs from expectation or from accepted scientific views. Anomalies lead scientists to revise a hypothesis or theory.
- Central Limit Theorem: The central limit theorem states that with a sufficiently large sample, the sample mean will be normally distributed. A normally distributed sample mean is necessary to apply the t test, so if you are planning to perform a statistical analysis of experimental data, it’s important to have a big sample.
- Conclusion: The conclusion is your determination of whether the hypothesis should be accepted or rejected. It is one of the steps of the scientific method.
- Control Group: The control group is the set of test subjects randomly assigned to not receive the experimental treatment. In other words, the independent variable is held constant for this group.
4. Progress in Science. This chapter examines theories and empirical findings on the overlapping topics of progress in science and the factors that contribute to scientific discoveries. It also considers the implications of these findings for behavioral and social science research on aging. The chapter first draws on contributions from the ...
- Irwin Feller, Paul C Stern
- 2007
- 2007
Understanding Science 101. Testing ideas with evidence from the natural world is at the core of science. Scientific testing involves figuring out what we would expect to observe if an idea were correct and comparing that expectation to what we actually observe. Scientific arguments are built from an idea and the evidence relevant to that idea ...
Accuracy. Accurate measurements are those close to what they should be. We know the temperature of the human body is 37°C. An accurate thermometer will give us this value (unless we are poorly ...
Jan 30, 2020 · Types of Variables. Independent Variable: The independent variable is the one condition that you change in an experiment. Example: In an experiment measuring the effect of temperature on solubility, the independent variable is temperature. Dependent Variable: The dependent variable is the variable that you measure or observe.
