How Scientists Use Data to Make Conclusions

Collecting data is one of the most important parts of an experiment.

But collecting numbers isn't enough.

Scientists also need to figure out what those numbers actually mean.

That's where analyzing data and making conclusions comes in.

A conclusion should be based on the evidence collected during the investigation, not simply on what someone expected to happen.

Starting With the Data

Imagine I am testing whether the amount of sunlight affects plant growth.

I could measure the height of several plants over a few weeks.

My data might look something like this:

| Amount of Sunlight | Average Plant Height |
|---|---:|
| 4 hours | 12 cm |
| 6 hours | 16 cm |
| 8 hours | 20 cm |

Just looking at the table already shows a pattern.

The plants receiving more sunlight grew taller in this example.

But I shouldn't immediately jump to a conclusion.

I need to look carefully at the entire experiment.

Look for Patterns

One of the first things scientists do with data is look for patterns.

Maybe one group consistently has higher measurements.

Maybe the values increase as the independent variable increases.

Maybe there is almost no difference between groups.

Graphs can make these patterns easier to see.

For example, I could create a graph with the amount of sunlight on the x-axis and plant height on the y-axis.

A graph might make an increasing pattern much easier to notice than a table alone.

Compare the Results With the Prediction

Before the experiment, I might have made a hypothesis.

For example:

If plants receive more sunlight, then they will grow taller.

After collecting the data, I can compare my results with that prediction.

If the plants receiving more sunlight generally grew taller, the results support my prediction.

If they didn't, then my prediction wasn't supported by the results.

That doesn't mean the experiment was a failure.

It means I learned something from the evidence.

Conclusions Should Use Evidence

A strong conclusion should explain what the data showed.

Instead of saying:

The plants grew better.

I could say:

The plants that received more sunlight had greater average heights during the experiment.

That statement is much stronger because it connects directly to the measurements.

Scientists use evidence from their observations and data to support their conclusions. (sciencebuddies.org)

Don't Ignore Unexpected Results

Sometimes one result doesn't match the others.

For example, maybe most plants receiving more sunlight grew taller, but one plant barely grew.

I shouldn't simply ignore that plant because it doesn't fit my conclusion.

I would want to investigate why.

Maybe the plant was unhealthy.

Maybe I measured it incorrectly.

Maybe it didn't receive the same amount of water.

Unexpected data can sometimes reveal problems with an experiment or lead to new questions.

Data Doesn't Always Prove Everything

Another important lesson is that one experiment doesn't automatically prove a huge scientific claim.

If my plants grew taller with more sunlight, I could say that my results support a relationship between sunlight and plant growth.

But there could still be other factors involved.

Maybe the experiment needs more trials.

Maybe I should test more plants.

Maybe I should repeat the experiment under different conditions.

Scientists have to think about the limitations of their experiments instead of pretending the data says more than it actually does.

What If the Data Doesn't Match the Hypothesis?

This is one of the most interesting situations.

Suppose I predicted that more sunlight would make plants taller.

But my results showed almost no difference.

I wouldn't change the data to make my hypothesis look correct.

Instead, I would report what actually happened.

Then I could ask why.

Maybe the plants already received enough sunlight.

Maybe another variable affected their growth.

Maybe my hypothesis was wrong.

That result could lead to a completely new experiment.

A Good Conclusion Connects Everything

A strong conclusion connects the original question, the data, and the evidence.

For example:

Question: Does the amount of sunlight affect plant growth?

Prediction: More sunlight will cause greater growth.

Data: Plants receiving more sunlight had greater average heights.

Conclusion: The results support the prediction because the plants receiving more sunlight grew taller on average.

This creates a clear chain from the question to the evidence.

Reflection

Before learning more about scientific conclusions, I thought a conclusion was basically just saying whether my experiment worked.

Now I understand that it is more about explaining what the evidence shows.

Scientists have to look for patterns, compare results, consider unexpected data, and connect their conclusions to actual measurements.

The most important lesson is simple:

The data should determine the conclusion—not the conclusion determine the data.

That's what makes an experiment useful.