Download Chapter 2 Vocabulary

Chapter 2 Vocabulary
2. Curiosity -- A
desire to learn about
the world around
you.
1. Open-minded & Skeptical-Willing to consider ideas but
with an attitude of doubt.
6. Awareness of Bias -- An
awareness of factors that
may influence how you
observe or evaluate data.


3. Honesty -Truthfully reporting
observations and
data.
4. Ethics -- Knowing
right from wrong.
Attitudes
that help
you think
scientifically

Personal Bias – Influenced by my
likes and dislikes
Cultural Bias – Influenced by where
& how I’m raised as well as my beliefs
Experimental Bias – Mistakes in the
design of the experiment that makes
one outcome more likely than
another.
5. Creativity -Inventive ways to solve
problems or coming up
with new things.
Scientific Reasoning requires a logical way of thinking based on gathering and evaluating
evidence.
Two Types
Because scientific reasoning relies on gathering and
evaluating evidence, it is always objective reasoning.
YES
Objective—make
decisions and draw
conclusions based on
available evidence.
Deductive Reasoning – is a way to explain
things by starting with a general idea and
then applying the idea to a specific
observation.
NO
Subjective – means
personal feelings have
entered into a decision
or conclusion.
Inductive Reasoning—uses specific
observations to make generalizations.
Example: Because all triangles
have three sides and a school
crossing sign has three sides, then
a school crossing sign is a triangle.
The rules of a triangle are being
applied to the sign to show that
the sign is a triangle.
Example: Many birds fly toward
the equator in the fall. Because I
have seen this many time, I
generalize that birds prefer warm
weather or need warmer weather
to survive.
Faulty Reasoning – Drawing a conclusion based on too little data can result in
faulty reasoning. This means that your reasoning might lead you to the wrong
general idea.
“The elm tree on my block has Dutch elm
disease. So does the one on your block.
That means that all elm trees have Dutch
elm disease.” This statement sounds silly
because you know that there are elm trees
that do not have Dutch elm disease. It is an
example of an overgeneralization,
or drawing a conclusion with too little data.
Another type of faulty reasoning is making an
illogical conclusion, or inferring something
that is not based on the data. “It rained all last
week and now I have a cold. Rainy weather
must cause colds.” This statement is an
example of an illogical conclusion. Colds are
caused by virus infections, which have nothing
to do with rain.
A third type of faulty reasoning,
personal bias, occurs when a
conclusion is not based on data,
but on personal opinion. “Wooden
baseball bats hit the ball farther
than aluminum bats, because I
can hit farther with a wooden bat.”
Unless you have collected data
on the use of both types of bat by
different batters, you cannot
support the conclusion that a
wooden bat hits the ball farther
with data. The conclusion is only
your opinion.