Descriptive Statistics

If you were to tell a friend a specific story about something that happened over the weekend, you would probably not start with the most relevant information. Instead, you would begin to describe the setting and the people there and, generally, offer a bit of context. This is what descriptive statistics are used for. Descriptive statistics describe the raw data and are usually the first step in the data analysis process.

Explore our app and discover over 50 million learning materials for free.

- Addiction
- Aggression
- Approaches in Psychology
- Basic Psychology
- Biological Bases of Behavior
- Biopsychology
- Careers in Psychology
- Clinical Psychology
- Cognition
- Cognition and Development
- Cognitive Psychology
- Data Handling and Analysis
- Analysis and Interpretation of Correlation
- Binomial Sign Test
- Content Analysis
- Descriptive Statistics
- Distribution Psychology
- Inferential Testing
- Levels of Measurement
- Measures of Central Tendency Psychology
- Measures of Dispersion
- Non-Parametric Tests
- Observed Values and Critical Values
- Presentation of Quantitative Data
- Probability and Significance
- Raw data
- Scientific Data Analysis
- Statistical Tests
- Thematic Analysis
- Wilcoxon Signed-Rank Test
- Developmental Psychology
- Eating Behaviour
- Emotion and Motivation
- Famous Psychologists
- Forensic Psychology
- Gender
- Health Psychology
- Individual Differences Psychology
- Issues and Debates in Psychology
- Personality in Psychology
- Psychological Treatment
- Relationships
- Research Methods in Psychology
- Schizophrenia
- Scientific Foundations of Psychology
- Scientific Investigation
- Sensation and Perception
- Social Context of Behaviour
- Social Psychology
- Stress

Lerne mit deinen Freunden und bleibe auf dem richtigen Kurs mit deinen persönlichen Lernstatistiken

Jetzt kostenlos anmeldenNie wieder prokastinieren mit unseren Lernerinnerungen.

Jetzt kostenlos anmeldenIf you were to tell a friend a specific story about something that happened over the weekend, you would probably not start with the most relevant information. Instead, you would begin to describe the setting and the people there and, generally, offer a bit of context. This is what descriptive statistics are used for. Descriptive statistics describe the raw data and are usually the first step in the data analysis process.

- We will start by learning the definition of descriptive statistics.
- Moving on from this, we will look at descriptive statistics in psychology, and we will cover some descriptive statistics examples.
- Then, we will look at some descriptive statistical analyses used in psychology.
- Last, you will learn the difference between descriptive and inferential statistics.

Descriptive statistics allow researchers to create a preliminary summary of the raw data. For this reason, they can also be referred to as summary statistics. Descriptive statistics are the first step in data analysis and provide valuable information for choosing the correct statistical test.

Descriptive statistics is a form of statistical analysis utilised to summarise a dataset.

As you can probably deduce from its name, descriptive statistics describe the main aspects of the data. They 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.

Researchers, however, do not conclude from descriptive statistics. To go beyond description and infer results, researchers use inferential statistics.

Descriptive statistics describe raw data, and inferential statistics make predictions about a larger population.

Descriptive statistics are usually presented graphically, either on tables, frequency distributions, histograms, or bar charts.

Generally, descriptive statistics are used in psychology research to summarise datasets.

However, descriptive statistics cannot be used to make inferences or generalisations about broader populations.

The frequency distribution describes the number of observations for a possible variable value. This information is often displayed in frequency tables.

Imagine a study looking into the relationship between two variables: hair colour and nationality. A frequency table would look like this:

Hair Colour | Frequency | Nationality | Frequency | |

Black | 7 | Ireland | 5 | |

Brown | 6 | England | 15 | |

Blonde | 14 | Wales | 5 | |

Ginger | 3 | Scotland | 5 | |

Total Sample | 30 | Total Sample | 30 |

From such a table, researchers can state that 14 individuals within the sample were blonde and that 5 were Irish.

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.

- Mean: adding all the values together and dividing by the total number of values
- Median: placing the dataset values in numerical order and identifying which is the middle number
- Mode: most common value in the dataset

Let's consider an example to understand central tendency. Imagine a study looking into the relationship between exam performance and revision time.

The raw data of 10 participants may look like this:

Participant Number | Time Revised (in hours) |

1 | 6 |

2 | 3 |

3 | 7 |

4 | 5 |

5 | 6 |

6 | 9 |

7 | 5 |

8 | 6 |

9 | 6 |

10 | 4 |

The mean (M) is the number one gets by adding all values together and dividing them by the total number. In this example, the mean amount of hours the sample studied is:

(6 +3 + 7 + 5 + 6 + 9 + 5 + 6 + 6 + 4) / 10 = 5.7 hours.

When reporting more than one means, it is written the following way: The average score of revision time among medical students was higher (*M* = 8.7) than in philosophy students (*M* = 5.6).

The numbers need to be placed in sequential numerical order to find the median, and the median is the middle number. In this case, it is:

3, 4, 5, 5, 6, 6, 6, 6, 7, 9. In this example, the median is 6

The mode refers to the most popular score in the data, which in this example is six because it reflects the data of 4 participants.

Measures of variability are meant to describe the amounts of differences within the data set. It's somehow the opposite of the central tendency.

There are four types of variability measures:

- Range: the highest value minus the smallest value
- Interquartile range: the difference between the median value calculated in the second half and first half of a dataset
- Standard deviation (sd): the average distance of a data point from the mean
- Variance: also measures the average distance of a data point from the mean, but it is calculated differently

Let's consider the example above.

Participant Number (N) | Time Revised (in hours) |

1 | 6 |

2 | 3 |

3 | 7 |

4 | 5 |

5 | 6 |

6 | 9 |

7 | 5 |

8 | 6 |

9 | 6 |

10 | 4 |

The range would be the highest score, 9, minus the lowest score, 3. Therefore, the range in this example is 9 - 3 = 6.

The interquartile range is the difference between the median values calculated in a dataset's first half and second half. The first half of the dataset would be 3, 4, 5, 5, and 6, while the second would be 6, 6, 6, 7, and 9. The median of the first half is 5, and the median of the second half is 6. Therefore the interquartile range is 6 - 5 = 1.

The standard deviation and the variance are slightly more complex to calculate; they measure the distance of a given data point from the mean.

A small variance or standard deviation suggests that the scores do not vary too much from the mean. On the contrary, a high variance or standard deviation indicates that the data is widely spread from the mean.

When writing psychology reports, the mean and standard deviation are the most commonly reported descriptive statistic.

A measure of position identifies the position of a given value from the other values. Quartiles and percentiles are used to measure position.

Percentiles, for example, divide the data into four categories: the 25^{th}, the 50^{th} and the 75^{th} percentile. When calculating percentiles, values need to be put in ascending order. In this way, researchers can establish which scores are associated with the different percentiles.

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.

This data provides information about the distribution of data, which is crucial for later statistical analyses. If data is skewed, non-parametric tests may be used for statistical analysis.

As you learned, descriptive statistics offer information about a specific dataset. And while these are helpful, psychologists also need other statistical tests to draw conclusions. For this, psychologists use inferential statistics. These are based on probabilities and let researchers test hypotheses and draw conclusions about populations.

Let's consider studying ice cream consumption rates across the year. Descriptive statistics may suggest that more ice cream is consumed in July than in January. And although it may be tempting to conclude that ice cream consumption is lower in January compared to July, this would not be accurate.

In order to make such a statement, one would need to test whether there is a significant difference between the means of ice cream consumption in both months. And this can only be achieved through inferential statistics.

- Descriptive statistics is a form of statistical analysis utilised to summarise a dataset.
- There are four main types of descriptive statistics: measures of frequency, central tendency, variability or dispersion, and measures of position.
- The most commonly reported descriptive statistics are the mean and range.
- Descriptive and inferential statistics have different uses, e.g. the first is used to summarise data and the latter is used to make inferences.

*M* = 22.8 & *SD* = 8.12) ”.

What are descriptive statistics?

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.

What are the benefits of measuring descriptive statistics?

These can be beneficial as they provide researchers with information about potential relationships between variables and statistical tests that could be appropriate to test the hypotheses proposed.

Where can you find data concerning the *N* of males and females in a sample?

Frequency table

What statistical information do tests measuring central tendency tell us?

They give a single value that summarises an average representing the entire dataset.

Here is an example dataset, calculate the mean, median and mode: 2, 7, 5, 3, 9, 12, 3

Mean - 5.86 (2 d.p), Median - 5, Mode - 3

Which is the most commonly reported central tendency measurement and how is it reported?

Mean (*M* = x).

Already have an account? Log in

Open in App
More about Descriptive Statistics

The first learning app that truly has everything you need to ace your exams in one place

- Flashcards & Quizzes
- AI Study Assistant
- Study Planner
- Mock-Exams
- Smart Note-Taking

Sign up to highlight and take notes. It’s 100% free.

Save explanations to your personalised space and access them anytime, anywhere!

Sign up with Email Sign up with AppleBy signing up, you agree to the Terms and Conditions and the Privacy Policy of StudySmarter.

Already have an account? Log in

Already have an account? Log in

The first learning app that truly has everything you need to ace your exams in one place

- Flashcards & Quizzes
- AI Study Assistant
- Study Planner
- Mock-Exams
- Smart Note-Taking

Sign up with Email

Already have an account? Log in