Scientific Investigation + CER Flashcards

Vocabulary flashcards covering the complete quiz set for Scientific Investigation, Experimental Design, Data Interpretation, and Claim-Evidence-Reasoning (CER).

Scientific Method

Steps scientists use to investigate questions and solve problems using evidence.

Scientific Method Order

Observation -> Question -> Hypothesis -> Experiment -> Data Collection -> Conclusion

Observation

Information gathered directly using the senses or scientific tools.

Inference

A logical explanation based on observations and prior knowledge.

Observation vs. Inference

Observation = what you directly notice/measure; inference = what you conclude from it.

Observation Example

'The plant is $12\,\text{cm}$ tall' is an observation because it was directly measured.

Inference Example

'The plant probably did not get enough light' explains an observation, so it is an inference.

Question Development

Forming a specific scientific question that can be tested with data.

Testable Question

A question that can be answered by collecting measurable or observable data.

Testable Question Example

How does water temperature affect the time sugar takes to dissolve?

Non-Testable Question Example

Which flower is the prettiest? It is opinion-based, not scientifically measurable.

Hypothesis

A testable, falsifiable prediction about what will happen in an investigation.

Falsifiable

A hypothesis must be able to be shown wrong if evidence does not support it.

Hypothesis Format

If [IV changes], then [DV will change], because [scientific explanation].

Hypothesis Example

If hours of light increase, plant height will increase because light provides energy for photosynthesis.

Experiment Design

Planning a fair procedure to test a hypothesis while controlling other factors.

Data Collection

Recording measurements and observations during an investigation.

Conclusion

Explains what the results mean and whether the data support the hypothesis.

Does a hypothesis get 'proven'?

No. Data can support or fail to support a hypothesis; one experiment does not prove it absolutely.

Variable

Any factor that can change in an experiment.

Independent Variable (IV)

The factor intentionally changed or manipulated by the researcher.

IV Memory Trick

IV = I change it.

Dependent Variable (DV)

The measurable outcome or response caused by changes in the IV.

DV Memory Trick

DV = Data I measure.

How does X affect Y?

Usually X = independent variable and Y = dependent variable.

IV Example

If testing how light affects plant height, hours of light is the IV.

DV Example

If testing how light affects plant height, plant height is the DV.

Controlled Variables / Constants

Conditions kept identical across groups or trials.

Why Keep Constants?

So changes in the DV are more likely caused by the IV rather than another factor.

Constant Example

In a fertilizer test: plant type, soil, pot size, water, and light could stay constant.

Control Group

The baseline group used for comparison.

Control Group Treatment

The control group usually does not receive the experimental treatment.

Control Group Example

In a fertilizer experiment, plants receiving 0 fertilizer can be the control group.

Experimental Group

The group that receives the treatment or tested level of the IV.

Control Group vs. Constant

Control = comparison GROUP; constant = CONDITION kept the same.

Controlled Experiment

An experiment where one main factor changes while other important conditions stay the same.

Why Change Only One IV?

So you can determine which factor caused the change in the DV.

What if two variables change at once?

You cannot clearly tell which variable caused the observed result.

Replication

Repeating trials or repeating an experiment.

Why Replicate?

To see whether results are consistent and not just caused by chance.

Sample Size

The number of subjects, organisms, objects, or observations being tested.

Why Use a Larger Sample?

It reduces the effect of unusual individuals and usually makes results more reliable.

Small Sample Problem

One unusual subject can strongly affect results and may not represent the population.

Random Sampling

Selecting subjects by chance so eligible members have a fair chance of being chosen.

Why Use Random Sampling?

It reduces selection bias and helps the sample represent the population.

Random Sampling Example

Randomly selecting 30 names from a complete class list.

Biased Sampling Example

Choosing only volunteers or only the first people who arrive.

Bias

A systematic influence that unfairly affects data collection, analysis, or conclusions.

How to Reduce Bias

Use random sampling, consistent procedures, objective measurements, and do not cherry-pick data.

Cherry-Picking Data

Selecting only results that support expectations while ignoring other valid data.

Why Is Cherry-Picking Bad?

It introduces bias and gives a misleading picture of the results.

Reliable Results

Results that are consistent when measurements or experiments are repeated.

How to Improve Reliability

Use larger samples, repeated trials, and consistent procedures.

Source of Error

Something that may make experimental results less accurate or reliable.

Human Error

A person makes a mistake, such as misreading a scale or recording the wrong value.

Calibration Error

A measuring instrument consistently gives inaccurate readings.

Systematic Error

An error that affects measurements in a consistent direction or pattern.

Uncontrolled Variable

A factor that should have stayed constant but changed during the experiment.

Qualitative Data

Descriptive, non-numerical observations.

Qualitative Memory Trick

Qualitative = qualities.

Qualitative Examples

Color, texture, odor, shape, appearance, and behavior.

Qualitative Example

The solution became cloudy.

Quantitative Data

Numerical measurements or counts.

Quantitative Memory Trick

Quantitative = quantity/numbers.

Quantitative Examples

Mass, temperature, length, time, volume, and number of cells.

Quantitative Example

The solution reached $31\,^\circ\text{C}$.

Qualitative vs. Quantitative

Qualitative describes; quantitative measures or counts.

Reading a Data Table

Check the title, row/column labels, units, values, and patterns.

Why Check Units?

Numbers need units to show what was measured, such as $\text{cm}$, $\text{g}$, $\text{mL}$, or seconds.

How to Find a Trend in a Table

Compare how the DV changes as the IV changes.

Highest Value in a Table

Find the largest numerical value in the requested row or column.

Lowest Value in a Table

Find the smallest numerical value in the requested row or column.

Change Between Two Values

Subtract the earlier/starting value from the later/ending value.

Graph X-Axis

Usually shows the independent variable.

Graph Y-Axis

Usually shows the dependent variable.

Graph Title

Should clearly describe the relationship or data being displayed.

Graph Units

Units should appear with axis labels when measurements have units.

Line Graph

Useful for showing trends or changes over continuous values such as time or temperature.

Bar Graph

Useful for comparing separate categories or groups.

Data Table

Organizes exact recorded values in rows and columns.

Increasing Trend

As the IV increases, the DV generally increases.

Decreasing Trend

As the IV increases, the DV generally decreases.

No Clear Trend

The data do not show a consistent relationship between the variables.

Trend

The overall pattern shown by the data.

Pattern

A repeated or noticeable relationship in a set of data.

Outlier

A data point noticeably different from the overall pattern.

Should an outlier always be deleted?

No. Investigate it first; it may be real or may indicate an error.

Graph Evidence

Specific numerical points or a clearly described trend taken directly from the graph.

Strong Graph Evidence Example

At $10\,^\circ\text{C}$ it took $95\,\text{s}$, but at $60\,^\circ\text{C}$ it took $25\,\text{s}$.

Graph Interpretation Tip

State what changed and use specific values when possible; do not just say 'it went up.'

If a graph slopes downward

As the x-variable increases, the y-variable generally decreases.

If a graph slopes upward

As the x-variable increases, the y-variable generally increases.

CER

Claim-Evidence-Reasoning; a structure for explaining scientific results.

Claim

A clear, concise statement that answers the investigation question.

What should a claim NOT do?

It should not just repeat the question without answering it.

Evidence

Specific data from the investigation that support the claim.

Strong Evidence

Relevant measurements, observations, or specific graph/table trends.

Weak Evidence

'The graph proves it' with no values, observations, or clear trend.

Reasoning

Explains HOW and WHY the evidence supports the claim using scientific principles.

Evidence vs. Reasoning

Evidence = what the data show; reasoning = why those data support the claim.