Characteristics of Science-part 1

http://web2.newtown-h.schools.nsw.edu.au/Science/CommonFolder/scienceindex.html

Hypothesis – based on ObservationExperimentation – Testing the hypothesisConclusions used to revise hypothesis

OVER AND OVER AND OVER…

When data from multiple experiments and multiple sources support the same hypothesis = Theory

Experimental Design

Theory vs Law

Theory– Based on substantial evidence – an

accepted hypothesis

–Explains observed facts (how nature works)

– Generally accepted by the field– Provides a basis to build upon

Then what is a Law?

Also based on substantial evidence. Describes behavior of nature – what

will happen Does not attempt to explain why Often follows a mathematical formula

*Law of gravity *Avogadro’s Law

*Charles’ Law *Newton’s Law of motion

Designing an Experiment Begin with an observation Create an explanation =

(What is the “null” hypothesis?)

Test the hypothesis = experiment– Data the hypothesis

– Data does not support –

disproven Try, try again!!

hypothesis

may support does not “prove”

Designing an Experiment

Hypothesis should define your independent & dependent variables

Testing the response of the dependent variable to changes in the independent variable

Important to keep all other influences under control (controlled variables)

Error vs BiasExperimental Error

– Random - part of every experiment• Minimize but cannot avoid entirely• Measurement imprecision• Sample variability• Affects reproducibility

– detected (and corrected for) by statistical analysis. • Simplest form – average of many trials

Avoiding Bias – it’s in the Design

Control group (can’t compare apples to oranges) – ex: men vs women

Method/Measurement • Tools – accuracy or sensitivity• Human – awareness (white coat

effect), memory, expectation (placebo effect)

Conducting the Experiment

Record data carefully Note any mistakes or problems NEVER throw out “bad” data!

It may be the most important part!!!

Selection & Use of Tools

Tools*Proper use

*Safety

Characteristics of Science-part 2

Select the best tool Accuracy – by design

– Beaker vs graduated cylinder Precision –

– *use the most precise tool available (minimizes error)

– *use it correctly!• 1 estimate past markings

Select the best tool

Range– Too large – inaccuracies for small units– Too small – requires multiple

measurements (more error)

Safety!–Chemicals

•Projectiles–Glassware

»Flames»Heat

»Fumes

Protective Gear Goggles are a must!

– Spills/splashes; Projectiles; Fumes; shattered glass

Lab aprons

– Spills

Heat & Flames

Avoid flames when possible – use other heat sources (oven, hot plate, etc)

When unavoidable – WEAR GOGGLES – avoid dangling clothing, tie back hair

chemistry.about.com

Ewww that smell!

Some fumes are unpleasant – others are dangerous!

Use properly vented fume hood Room fan should also be available Proper chemical goggles can protect

against fumes getting into eyes Protective mask under extreme conditions

No Food or Drink

No eating or drinking near lab area Always wash hands after handling

chemicals or equipment Keep hands away from face & eyes

Emergency procedures – just in case….. Shower

Eye Wash

Fire Extinguisher

Fire Blanket

Characteristics of Science-part 3Analyzing Data

Human error – a mistake• Did not follow procedure • Contaminated the sample• Misread the instrument• Calculations done incorrectly

Can be eliminated with training & experience – time for a re-do!

Consider Sources of Error

Sources of Error

Random Experimental Error– No measurement can be made to infinite

precision– Can be introduced by human – but

unavoidable (ex – use of stopwatch)– Not a mistake – a limitation in the

methodology– Impact removed by averaging multiple

trials

Sources of Error

Systemic Error = Bias– Part of the experimental design

• Poor instrument• Poor design

– Must be avoided at all costs!• Produces skewed data• Always skewed in the same direction• Reproducible error – wrong every time

Analyzing Data

Multiple trials – the more, the better Averaging data reduces effects of random

error % Error used to assess accuracy of an

experiment when a standard value is available:

% error = (standard value –exp. value) x 100 standard value

Analyzing Data Standard deviation - examines error

more closely Reported as average ± S.D. Large S.D. = significant random error

(possibly careless technique) Smaller S.D. = less random error –

more careful technique “Overlap”- two measurements were not

shown to be different.

Analyzing Data

Looking for trends - Graphing & slope DRY MIX

– Dependent variable on Y axis– Independent variable on X axis

• Positive (direct) correlation• Negative (inverse) correlation

Slope calculates the rate of change

Rise over run

Sharing of Data – an important part of developing a Theory or Law Function of the scientific community:

To challenge – lead to revision of an hypothesis or theory

To support – add to accumulating evidence in support of a theory

Interpret with care!

Done well - Leads to – revised hypothesis– more testing– Ultimately better understanding & a

working theory Done poorly – Leads to

– Misunderstanding/confusion– Often dangerous misconceptions

Characteristics of Science-part 4

Models are a tool that• describes connections between

multiple systems.• provides a simplified

explanation• may be used to predict• can be applied to many similar

situations

Developing and Using Models

Models: Their Advantages & Limitations Advantages

– Easily understood– Apply to many areas– Communication/teaching tools

Disadvantages– It’s not always that simple!

• Ex: Bohr’s model of the atom vs Quantum theory