Data inference is the process and method by which data can be studied under different circumstances and manipulated according to the requirements of the researcher. Any statistical analysis produces raw data that needs to be studied.

These data need to be modified in a presentable form so that further conclusions can be drawn from these data. Therefore, for this, the researcher needs to learn different methods of data presentation.

With the increasing use of computers in statistics, there are many software and programs available today that help in data inference. These tools can be used by the researcher to present the results in different formats and also help her to perform the necessary calculations of the data.

Spreadsheets are very handy data output tools that can help the researcher to quickly perform simple calculations and check the data. Simple statistical analysis and statistical parameters such as mean, median, mode, range, etc. can be easily found using spreadsheets.

For example, in a physics experiment, if the time interval between two events is specified, it is always better to average the readings to eliminate random errors. When these data points are entered into a spreadsheet, their mean, standard deviation, etc. can be easily found. This makes it easier to record the results and also helps to identify any outliers and anomalies.

Data output also includes the presentation of the data. For example, if a researcher is studying the impact of a certain disease on people of different age groups, she can use a pie chart to indicate the percentage of sick people in different age tables.

This will immediately give a graphical representation of which age group is most susceptible to that disease. If the researcher needs to include absolute numbers, they can use a bar chart.

The choice of data output and presentation format should be based on the conclusions drawn from the study. In the above case, if the study aims to show that children are most susceptible to the disease, then a pie chart may be the best option. However, if the purpose of the study is to show that the disease spreads most rapidly among the elderly, then a bar chart may be the best option.

Data output is central to statistical analysis and is an integral part of the experiment. When done correctly, output can easily reveal the strengths of a study.