I think it depends on the type of data we have as to whether we need statistics in order to understand it. For example, whether we have qualitative data or quantitative data can make a difference as to whether we need to use statistics to interpret and understand our findings.
In the case of qualitative data it may not be necessary to use statistics as this type of data often comes from interviews or case studies. We need to use qualitative research in psychology as a basis for direct experience, as a result of this we are often not measuring group means and therefore would not need to use statistics to understand our data. When using data such as interview transcripts it would be easier to go through the information in front of you and evaluate it to gain an understanding. However, the problem with this method is that it is open to a lot of biases. For example, the researcher may be biased towards their hypothesis and so may only take into account the parts of the data that are in line with their hypothesis. If a researcher did want to be able to use statistics with qualitative data they would be able to operationalise the variables making data quantitative. Research conducted into aggression and pro-social behaviour used this method by categorising different types of behaviour into groups and giving certain behaviours certain scores. For example ‘hitting’ would be categorised as aggressive behaviour and given a score of 5 (Ihori et al, 2007)*. The mean score could be calculated and statistical tests used to test the significance of the hypothesis. Even so, in the case of qualitative data I don’t really think we need to use statistics to understand our data but it may make the analysis simpler for the researcher and is also a more scientific method to use.
Quantitative data, however, is different in that statistics is an important part of understanding the data. If we just presented the raw data then it would be very difficult to comprehend. However by performing the correct statistical test we can simplify a lot of confusing numbers, thus making it simpler to understand. For example, the p value produced (e.g. p<.01) is a lot quicker and easier to understand than looking through hundreds of numbers and trying to decide if a result is significant from the raw data. However, it may also be useful to present the results of statistical tests but also make the raw data readily available so that people can see the bigger picture. Kling et al (1999)** conducted a study in which they analysed the relationship between self-esteem and gender. From this they were able to compare the mean self-esteem scores and compare these to the two genders to see how they differed. Without the use of statistics this study would have produced a lot of different numbers which would have been more difficult to understand. However, by statically analysing the data they were able to produce graphs to accompany the final results, making it more understandable.
So, to move on towards concluding, my opinion is that statistics can be important for understanding your data. However, I’m going to sit on the fence on this one and say that it completely depends on the type of data you have as to whether statistics will be helpful or not.
** http://www.ncbi.nlm.nih.gov/pubmed/10414226 (abstract) … Or … It’s in the research methods book from last year.