Page 276 - Ai_417_V3.0_C9_Flipbook
P. 276
Methods of Data Interpretation
Data interpretation is the process of making sense out of a collection of data that has been processed. This
collection may be present in various forms like bar graphs, line charts and tabular forms and other similar forms.
There are two ways to interpret data-
Quantitative Data
Data Interpretation Interpretation
Methods
Qualitative Data
Interpretation
Quantitative Data Interpretation
It is the process of analysing and understanding quantitative or numeric data. This type of data often comes
from surveys, experiments, and numerical measurements. Quantitative data provides statistical insights and
helps in identifying patterns and trends. It requires statistical methods and techniques like mean, median,
standard deviation, etc. to interpret the data. The interpretation of quantitative data focuses on measurable
outcomes and numerical relationships. It helps us answer questions like "when," "how many," and "how often".
For example, (how many) numbers of likes on the Instagram post.
Data Collection Methods in Quantitative Data Interpretation
Data collection methods in quantitative data interpretation involve systematic techniques like surveys and
experiments to gather numerical data. These approaches ensure data accuracy, facilitating reliable analysis and
inference across various fields such as social sciences and healthcare.
Following are some application areas of quantitative data interpretation:
● Interviews: Quantitative interviews play a key role in collecting information.
● Polls: A poll is a type of survey that asks simple questions to respondents. Polls are usually limited to one
question.
● Observations: Quantitative data can be collected through observations in a particular time period.
● Longitudinal studies: A type of study conducted over a long time.
● Survey: Surveys can be conducted for a large number of people to collect quantitative data.
274 Touchpad Artificial Intelligence (Ver. 3.0)-IX

