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Descriptive statistics are a form of statistical analysis that are utilised to provide a summary of a dataset. They can be summaries of samples, variables or results.
There are four main types of descriptive statistics that are discussed in further detail below.
Descriptive statistics allow researchers to provide a basic summary of datasets. The tests are usually carried out before carrying out statistical analysis that tests the hypothesis. These tests are beneficial as they provide researchers with information about potential relationships between variables and information regarding which statistical tests would be appropriate for testing the proposed hypothesis.
It is important to note that descriptive statistics provide information about the dataset and it is not appropriate to generalise to the general population.
The purpose of frequency statistics is to calculate the occurrence of variables, for example, the number of participants in a trial group vs. a control group, or the number of males versus females in a sample. This data is usually outputted in the form of frequency tables.
An example of a frequency table output could provide frequency statistics for two variables: gender and ethnicity. The table would indicate the number of participants classified in each sub-group of the research (N). The table would also provide statistical information regarding how much N (for each variable), represents the total sample in the form of percentages. An example of how this would be reported in research is “the study sample consisted of 216 females and 259 males (N = 474)”.
There are many different statistical tests used to measure central tendency. Measures of central tendency give a single value that is an average of the entire dataset, this is beneficial for large datasets. The three most commonly used are: mean, median and mode.
The mean is the most commonly reported form of descriptive analysis, and is usually written as, “The number of participants recruited in the study was 10, with a M age of 22.8”.
We can analyse measures of variability or dispersion using range, interquartile range, standard deviation and variance.
An example of how this would be reported is “The number of participants recruited in the study was 10, aged 18-27 (M = 22.8 & SD = 8.12) ”.
When writing psychology reports, the mean and standard deviation are the most commonly reported descriptive statistic.
Measures of position analysis are used to identify a singular value and its relation to other values within a dataset.
An example of descriptive tests that identify measures of position are quantiles. Quantiles are measured by numerically ordering values in ascending order. Quantiles separate populations/samples into intervals of equal sizes. This is done so that ranking of specific data points can be identified.
For example, percentiles is when data is split into 100ths and data points are observed within the different sections of the percentiles. For instance, if you are trying to identify the data point at 36%, then the values would be placed in ascending order and the value that is representative of 36% of the data would be identified.
The amount that interval quantiles are split into is relative to an appropriate number determined by the number of values within a dataset. This data provides information about the distribution of data, which is important for later statistical analyses. If data is found to be skewed then non-parametric tests may be used for statistical analysis, these concepts are explained further in other articles.
The purpose of descriptive statistics is to provide a summary of a dataset. However, it is also important for researchers to identify if the sample used in research is appropriate to generalise to the general population. Therefore, a general requirement of research is to carry out descriptive statistics and inferential statistics.
An example of an inferential statistic is hypothesis testing. This analysis involves forming a null hypothesis (no significant effect will be observed between variables) and using an appropriate statistical test to identify if there is a relationship between the variables.
If this is found to have a significant effect size then the null hypothesis is accepted. This implies that changes in the dependent variable are likely due to chance or other potential confounding variables rather than the independent variable. Therefore, the alternative hypothesis (expect to observe a difference between the variables) can be considered inapplicable and cannot be generalised to the population.
The four main type of descriptive statistics are: measures of frequency, measures of central tendency, measures of variability/dispersion and measures of position.
Descriptive statistics are a form of statistical analysis that is utilised to provide a summary of a dataset. These can be summaries of samples, variables or results.
Descriptive data are various forms of statistics that provide a summary of the data from research. For example, the mean is a measure of central tendency that is used to find the average value of variables/ data. Whereas inferential statistics are data that allows the researcher to identify if the sample/procedure used in research is appropriate to generalise to the general population. The output from hypothesis testing is an example of inferential statistics.
In psychology research the most common reported descriptive statistics is the mean and the range. An example of how this would be reported is “The number of participants recruited in the study was 10, aged 18- 27 (M = 22.8 & SD = 8.12) ”.
The purpose of descriptive statistics is to provide a summary of data from research and can highlight any potential relationships/trends between variables. Moreover, some descriptive statistics can be used to help identify what type of analysis should be done later, for instance, parametric versus non-parametric statistical analysis.
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