## 04 – IT Modelling / Reporting Experiments (Statistics)no comments

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# Hypothesis and Experimentation

## The scientific method

The hypothetico-deductive aspect of the scientific method focuses on the observation.  This observation leads to a guess or logical guess called the hypothesis that tries to explain how a system works.  From there, some predictions are made from this hypothesis and the experimentation or tests begin to try to prove it.

After the experimentation, the results can only be either consistent or inconsistent with the hypothesis.

These sets of experimentation will allow the hypothesis to be more consistent with the implementation of the project.  But it is important to link the results properly with the hypothesis.  This is where the statistics come in.

## Statistics

Statistics are use in many different industries. Statistics will allow us to make decisions about large numbers of subjects which we can be able to group into some sort of systems.  This way we can see patterns or data that is not visible through ‘static’ numbers.

It is extremely important to understand how statistics work.  This is due to the necessity to analyse the information inside them.  If we can not produce a proper statistical model, perhaps we won’t be able to make a good decision about our project. Also, if we can not understand statistics, there is no way we can see errors or disprove a theory or result.

### Graphs

Once we have developed the statistical models we also have the option of visualizing this data. Or perhaps analyzing more in depth the information provided.

Mean, Error, Percent Error, and Percent Deviation

All these arithmetical/statistical tools can help us to understand our data.  For example the Percent Deviation will allow us to understand or to see the whole extent of the data, not only the mean number.

σ =
 ∑d2 n-1

Percent Deviation

All statistical models are methods of obtaining the probability of success of our experiments which will help making a decision about our hypothesis or group analysis

## Reporting Experiments

Through the report is where the explanation about the study. Peter Harris (2008) points 5 elemental items for the report.

• What you did
• Why you did it
• How you did it
• What do you think it shows

This can then be translated to a formal document presentation like this:

• Title
• Abstract
• Introduction
• Method
• Results
• Discussion
• References
• Appendices

So, through this report we are intended to provide the information and the appropriate material. For this we also have to consider our reader.  Who is intended to see our information. This is important because perhaps we will have to give an introduction to our area of study. If we are presenting the document to Computer Scientists, perhaps we need to give and induction to Heritage or Visual Communication.

## Within museums

The statistics and the report provided is also an intrinsic part of the analysis. Before even starting to provide model experimentation, it is important to provide a hypothesis.  Something like:

• What are the main reasons why small museums don’t have access to big collections?
• How many visitors does each museum have per year/per day/per month?
• How many times does an expensive collection travel through different museums?

It is important to start analyzing this type of information in order to visualize the real requirements not only of the project but also of the museum. problem or situation.

### Bibliography

Brookshear, J. G. (2010). Computer science an overview. (11th ed.). Addison-Wesley,.

Harris, P. (Peter R. ). (2008). Designing and reporting experiments in psychology (3rd ed., p. 284). Maidenhead :: Open University Press,

McKillup, S. (2006). Statistics explained an introductory guide for life sciences (p. 267). Cambridge :: Cambridge University Press

Written by Javier Pereda on November 17th, 2011