How can you determine the success of an experiment?

1 Define your goal and hypothesis

Before you run an experiment, you need to have a clear goal and a testable hypothesis. A goal is a specific, measurable, achievable, relevant, and time-bound outcome that you want to achieve with your experiment. A hypothesis is a tentative explanation or prediction of how your experiment will affect your goal. For example, your goal might be to reduce the cycle time of a process by 10% in two weeks, and your hypothesis might be that changing the layout of the workstations will reduce the cycle time by 10%.

2 Design your experiment

Once you have your goal and hypothesis, you need to design your experiment in a way that minimizes bias and confounding factors, and maximizes validity and reliability. You need to decide what variables you will manipulate, measure, and control, and how you will collect and analyze data. You also need to determine how you will compare your experimental group (the one that receives the change) and your control group (the one that does not receive the change). You can use tools such as run charts, control charts, Pareto charts, and fishbone diagrams to help you design your experiment.

3 Run your experiment

After you design your experiment, you need to run it according to your plan. You need to follow the standard operating procedures, document the results, and monitor the process performance. You need to ensure that your experiment is conducted in a consistent and controlled manner, and that you collect enough data to draw meaningful conclusions. You also need to be aware of any potential risks, ethical issues, or unforeseen events that might affect your experiment.

4 Analyze your data

Once you run your experiment, you need to analyze your data to see if your hypothesis is supported or rejected. You need to use appropriate statistical methods and tools to test the significance of your results, and to quantify the effect size of your change. You can use tools such as histograms, box plots, scatter plots, and t-tests to help you analyze your data.

5 Interpret your results

After you analyze your data, you need to interpret your results and draw implications for your goal. You need to compare your actual results with your expected results, and explain why they are similar or different. You need to consider the strengths and weaknesses of your experiment, and the limitations and assumptions of your data. You also need to evaluate the practical significance and relevance of your results for your process improvement.

6 Communicate your findings

Finally, you need to communicate your findings and recommendations to your stakeholders and team members. You need to present your goal, hypothesis, experiment design, data analysis, results interpretation, and implications in a clear and concise way. You need to use visual aids such as graphs, tables, and charts to support your arguments, and to highlight the key takeaways and action steps. You also need to solicit feedback, answer questions, and acknowledge any gaps or uncertainties in your experiment.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?