Statistics come in many shapes and forms, whether it’s descriptive, such as the mean that provides us with a simple understanding of the data, inferential which are more complex, or as a visual representation such as a graph. All of these types of statistics are used within psychology to enhance our understanding of raw data in a research study, by collectively transforming it into more manageable data.
It is a common misconception that when qualitative data is collected statistics are not used to analyse it due to the data being non-numerical. This is not always the case. Often, qualitative data is collected via observations and used to write a report based on the findings, thus leaving it in a non-numerical form, such as case studies. A case study that had huge impact on the field of Psychology is Freud’s analysis of Anna O. However, on many occasions qualitative data is collected through nominal research, therefore is categorical, and is then transformed into quantitative data in order to analyse it easier. This could be done by creating a statistical table using the frequencies or by creating a pie chart etc, as demonstrated by Mendenhall, Beaver & Beaver, page 12.
Often, experimental error and variability within data can obscure meaningful differences. By just looking at the data without performing any statistical procedure it can be very difficult to identify a pattern or consistent differences between treatment conditions. For example, those who attended the Personality & Individual Differences lecture this week will recognise the 2 graphs shown below for Attention Blinks. It is very hard to identify a pattern on the graph which shows all the individual results, but after a statistical procedure to show the mean values the pattern is much clearer. Sometimes large differences can be identified without using statistics, but these mathematical procedures can help determine the extent of the difference more accurately.
However, although the use of statistics can help to understand data and identify patterns we may not naturally notice ourselves, it can also lead to confusion. Sometimes after using a statistical technique your results may look too good to be true, and they often are. Frequently, statistically significant treatment effects are not as meaningful in the real world as they initially appear. For example, studies have shown that if the average human consumes 20-30g of wine a day, it could reduce their risk of heart disease by 40% (Renaud & Logeril, 1992). This statistic looks amazing and encourages us to follow the suggestion and drink the recommended amount of wine per day. However, if in reality your susceptibility to getting heart disease is only 5% then this reduction is not really that significant practically, and the other health risks you may intensify by increasing your alcohol consumption may not be equal in comparison.
The aim of a researcher is not only to find evidence to support a hypothesis, but also to understand why this happens. Although statistics can be used to find cause and effect relationships it does not suggest anything about why these relationships exist. Statistics help to describe what the data has found, however it provides no insight as to what this means in reality, what this revelation can lead to, or why your data is like this. Therefore they can be helpful in many circumstances to analyse data and lead to the acknowledgment of findings that may not have been discovered otherwise, however they do not provide a complete picture of the findings and are not necessary in the understanding of data.