Trends/patterns/anomalous data and sources of error in data

A trend in data refers to the overall direction that the data seems to be going in. This could be an increase, decrease, or a sequence that repeats over time. It provides an overview of what is happening in the data over a period.
A pattern in data refers to a repeated sequence of observations. For instance, a regular increase or decrease, or cyclical events that recur at regular intervals. Patterns help us predict what might happen next in a series.
Identifying trends and patterns is a key aspect of data analysis in scientific research, as it can highlight regularities or predictabilities, contributing to hypothesis generation and testing.

Anomalous data refers to observations that fall outside the expected range or pattern of a data set.
Such data often arises due to measurement errors, instrumental malfunctions, or other uncontrolled variables, and is typically excluded from the final analysis to avoid inaccurate conclusions.
For an anomaly to be genuine, it must be replicable. If the same observation is consistently made under the same conditions, there may be a real phenomenon occurring that contradicts the current understanding or prediction.
Anomalous data can sometimes indicate a new or extraordinary scientific discovery, making its identification as important as recognizing trends and patterns.

Random errors are unpredictable variations that occur in all measurements. In human biology, this could result from little variations in a person’s heart rate or body temperature that are natural and cannot be controlled.
Systematic errors consistently skew results in a particular direction. If a measuring instrument, such as a thermometer, is consistently off by a small amount, that would create a systematic error.
Implementing quality control measures, such as calibration of instruments, cross-validation with other methods, and replication of measurements, can identify and minimize the impact of errors.
Recognizing potential sources of error is important in the interpretation of scientific data, as it influences the validity and reliability of the conclusions drawn.