General Science PRAXIS 5435
General Science PRAXIS 5435
General Science PRAXIS 5435
General Science PRAXIS 5435
General Science PRAXIS 5435
General Science PRAXIS 5435
General Science PRAXIS 5435
General Science PRAXIS 5435
General Science PRAXIS 5435
General Science PRAXIS 5435
...
General Science PRAXIS 5435
Questions and Answers
Identifying problems based on observations
✓ ~~~Causal question—what am I seeing, what hypothesis might I test to learn more
information about what I am seeing?
Forming and testing hypotheses
✓ ~~~Hypotheses make a prediction about what will happen upon experimentation, but will
not make inferences as to why something is happening. I.e. As amount of physical
exertion increases, heart rate will increase.
Development of theories, models and laws
✓ ~~~A theory is a thoroughly vetted explanation for phenomena. It is not a name taken
lightly, it is almost fact; no evidence has arisen to disprove the theory. A model is a type of
theory that attempts to explain how something works. I.e. heliocentric model. A law is a
theory that has been proven to be fact, such as the laws of motion. It has been affirmed
that no evidence will ever be brought forward to disprove a law. Hypotheses become
theories as they are tested and supported over time. Theories become laws when it is
clear no evidence will come forward to disprove them.
Experimental design, including independent and dependent variables, controls, and
sources of error
✓ ~~~The independent variable is the one and only thing about an experimental setup that I
am changing, in order to measure a dependent variable. I.e. I change the amount of
exercise, and heart rate changes as a result. Controls are what stays the same in my
experiment. I.e. how quickly I do jumping jacks, how many jumping jacks I do, that the
same person is doing the jumping jacks. Sources of error include anything that may be
changing data that is uncontrolled. I.e. food calorimetry lab, not all heat is being captured
efficiently by the water.
Process skills including observing, comparing, inferring, categorizing, generalizing,
and concluding
✓ ~~~Observing; making observations and asking causal questions.
Comparing; evaluate two phenomena in light of each other
,Inferring; using evidence to offer an explanation of why something is happening
Categorizing: using observations to organize information
Generalizing: knowing what can and cannot be included in a generalization
Concluding: drawing conclusions based on experimental evidence.
Nature of scientific knowledge: subject to change
✓ ~~~As new evidence is gathered, previous conclusions may change to fit the evidence.
We can't know things until we make hypotheses, and those hypotheses may be
disproven.
Nature of scientific knowledge: consistent with evidence
✓ ~~~We cannot call something a scientific finding unless it is based on experimental
evidence; anecdotal evidence is insufficient.
Nature of scientific knowledge: based on reproducible evidence
✓ ~~~Evidence has to be reproducible in an experimental setting. A one-time phenomenon
is not enough to come to scientific conclusions. I.e. "I drank a soda and did well on my
exam" does not make drinking soda a tried and true way to do well on an exam; it needs
to be experimentally tested.
Nature of scientific knowledge: includes unifying concepts and processes (e.g.,
systems, models, constancy and change, equilibrium, form and function)
✓ ~~~Consistent with previous knowledge. Fits with other systems and models. Is able to be
disproven and/or modified.
Common units of measurement (metric and English) including unit conversion and
prefixes such as milli and kilo
, ✓ ~~~Kilogram (kg) -- .5 kg in 1 pound
Second (s)
Kelvin (K)
Meter (m) - 0.3 m = 1 ft
Liter (L) - 0.9 L in 1 quart
Unit Prefixes from largest to smallest:
Giga
Mega
Kilo
Deci
Centi
Milli
Micro
Nano
Pico
Femto
Organization and presentation of data
✓ ~~~Bar graph to compare numbers in different groups.
Line graph to look at data over time.
, Pie chart to compare parts of a whole.
Histogram to compare continuous numerical groups.
Dot plot a modification of bar graphs.
Labels on axes.
Basic data and error analysis including determining mean, accuracy, precision, and
sources of error
✓ ~~~Mean: total divided by number of groups/individuals. Accuracy: correct results.
Precision: same results repeatedly. Sources of error: Why does my data not look how it
should?
Trends in data
✓ ~~~Skewed? Right or left? Continuous growth or shrinking?
Relationships between variables
✓ ~~~Correlation vs. causation. We often cannot conclude causation.
Predictions based on data
✓ ~~~What will happen in the future? What could cause a change in the data?
Drawing valid conclusions based on data
✓ ~~~Drawing correlations between variables while accounting for any error or confounding
factors.
Appropriate and safe use of materials (e.g., chemicals, lab specimens)
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