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After data has been described, compared, and graphed, the next step is interpreting the data to draw new conclusions and perhaps propose a new hypothesis for further testing. Often scientists will find that the patterns in their data bring up new questions for exploration.

If an experiment is designed well, the outcome (and collected data) should clearly prove or disprove the initial hypothesis. It is much easier and more common for a scientist to prove a hypothesis wrong than to prove it right. Finding that the hypothesis is incorrect helps rule out wrong ideas and is a very important step toward eventually finding an answer to larger questions that are being asked — and toward determining which hypothesis to test next.

The challenge at this stage is applying previous knowledge (perhaps from previous experiments) to understand what the patterns in the data — or the relationships between variables — mean. Rather than finding answers to all the questions, scientists often find themselves asking new questions and circling back to the hypothesis stage, preparing to test another hypothesis.