What if your hypothesis is proven wrong

Three types of experiments proposed by scientists Type 1 experiments are the most powerful. Type 1 experimental outcomes include a possible negative outcome that would falsify, or refute, the working hypothesis.

It is one or the other. Type 2 experiments are very common, but lack punch. A positive result in a type 2 experiment is consistent with the working hypothesis, but the negative or null result does not address the validity of the hypothesis because there are many explanations for the negative result. These call for extrapolation and semantics. Type 3 experiments are those experiments whose results may be consistent with the hypothesis, but are useless because regardless of the outcome, the findings are also consistent with other models.

Formulate hypotheses in such a way that you can prove or disprove them by direct experiment. The whole point of doing an experiment is to determine if something is true or not. In that sense, if your hypothesis is wrong, it doesn't necessarily mean that you're wrong. What matters is how you write-up your report. The results -- even if they're different from your hypothesis -- will demonstrate what you learned and how you might change the experiment next time. Make a list of everything that was wrong with the hypothesis.

Make a second list with the any information that was correct in the original hypothesis. Write a short paragraph about each area where the hypothesis was correct or incorrect for a thorough explanation. Use photos, if possible, to illustrate the areas in which the hypothesis was incorrect.

Write down the information that was discovered from the experiment. Record the actual results and how they differed from the original hypothesis. Include notes for future experiments on the same topic that can help explore the idea further. Next, identify problems that arose during the experimental process and follow that in the write-up with suggestions for improvements and future courses of action.

The key to crafting the section on future courses of action is to work systematically backward to ascertain where the error might have taken place and then to make corrections to see if changes in those gap areas might lead to different results. The write-up is necessary to document what happened during the experiment. It becomes part of the background literature surrounding the issue being questioned or experimented on.

Make slight changes in the process by methodically working backward, starting with a check on the analysis process. Was the analysis off? Sometimes experimental data are incorrectly assessed. That means you have to ascertain if the analysis is where the error lies.

For example, some physics experiments require mathematical calculations. If these calculations contain errors, then the analysis shows data that does not coincide with the hypothesis.

Correcting any mathematical calculations is a necessary step after any experiment, especially if they have a bearing on whether the data confirms the hypothesis. Besides mathematical calculation analyses, evaluations that center on comparisons, making predictions or making discoveries can occur. If analyses reveal discrepancies, check whether there were any errors in the comparisons, predictions or discoveries processes.

Rooting out these errors can alleviate any data-to-hypothesis discrepancies. Human error can skew experimental data, and human error can rear its head at the experimental stage — whether in setting up the experiment, running the experiment, observing the experiment or in tabulating the experimental results.

Minimizing errors at the experimental stage can affect whether the results confirm the hypothesis or not.