DESIGNING AN EXPERIMENT. Scientific Inquiry – the process of gathering evidence about the natural world and giving explanations based on evidence. DESIGNING

DESIGNING AN

EXPERIMENT

Scientific Inquiry – the process of gathering evidence about the natural world and giving explanations based on evidence.

DESIGNING AN EXPERIMENT

Pose QuestionsOften times when we make observations, we question why or how certain things happen

STEPS FOR DESIGNING AN EXPERIMENT

Define the ProblemAfter posing your questions, you should choose one problem that can be tested


Develop a HypothesisA possible answer to a scientific question that can be tested


ExperimentCreate a controlled experiment that follows reliable scientific principles to test a hypothesis and prevents experimental bias


Controlled experiment – a scientific experiment in which only one variable is changed at a time


Variables Factors in an experiment that can change


Constant/ Controlled Variable A factor or condition that stays the same in an experiment

Can be more than one thing


Independent Variable The only factor or condition that is intentionally changed by an investigator in an experiment.

The factor you wish to test


Dependent VariableA factor or condition that might be affected as a result of change in the independent variable.

Factor you measure to gather results


Experimental Bias – an error in the design of the experiment


Collect and Interpret DataBefore starting your experiment, determine what observations you will make and what data you will gather


Qualitative Observations/Data – use the 5 senses Example: see, hear, smell, taste, feel

Quantitative Observations/Data – use numbers Example: How much? How many?


Tools such as data tables, diagrams, graphs, and models can help you interpret data by revealing patterns or trends


Draw ConclusionsConclusion – a summary of what you have learned from an experiment

A conclusion is unreliable if it comes from the results of one experiment – many trials are needed before a hypothesis can be accepted as true


A reasonable conclusion is based on data and evidenceExamine the data objectively to see if the results support or fail to support your hypothesis


Faulty reasoning, or experimental bias, occurs when the conclusion is not supported by the data


Hypotheses are valuable even when they are not supported by the data – they can lead to further investigation

When you detect faulty reasoning, you need to obtain additional information to determine whether the conclusion is valid or not


Overgeneralization - draw a conclusion based on too little data

When a conclusion about a whole class of things is based on very few samples

3 TYPES OF FAULTY REASONING/EXPERIMENTA

L BIAS

Overgeneralization

Example: “German Shepherds shed their fur, and Springer Spaniels shed their fur. Therefore, all dogs shed fur.” This is an overgeneralization. Two types of dogs shed, but some kinds of dogs do not shed, such as Poodles.


L BIAS

Illogical conclusion – making an inference that is not supported by data

Often indicate a cause-and-effect relationship that does not exist, based on coincidental events


L BIAS

Illogical Conclusion

Example: Suppose you break a mirror and then fall on your way to school, losing your homework. You conclude that “Breaking mirrors causes bad luck.” This is an illogical conclusion based on two unrelated incidents.


L BIAS

Personal Bias – basing conclusions on opinion rather than information

Can lead to conclusions that are actually contradicted by the data

Determine whether the author or speaker is trying to argue for a particular point of view


L BIAS

Personal Bias

Example: Your friend says, “I don’t like doing labs. Chemicals smell bad.” This is not a scientific statement; it is purely opinion.


L BIAS

CommunicateScientists share their results with others through writing and speaking

When scientists share the results of their research, they describe their procedure and data so that others can repeat their experiment


Documents

DESIGNING AN EXPERIMENT. Scientific Inquiry – the process of gathering evidence about the natural world and giving explanations based on evidence. DESIGNING