Population and Sample

• • Population a complete group of people or animals
• Target population - group being studied
• Sample

  • part of a population selected for research
  • must be representative to generalize
• Sampling techniques are methods used to select a subset of individuals or items from a larger population for the purpose of research or statistical analysis.
• sampling techniques can be divided into two types:

  • Probability or random sampling
  • Non- probability or non- random sampling
• Probability Sampling:

  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Sampling
  • Systematic Sampling
  • Multi Stage Sampling
• Non-probability sampling:

  • Quota Sampling
  • Convenience Sampling
  • Judgment Sampling
  • Snowball Sampling
• Probability Sampling - involves selecting individuals or items from a population in such a way that each member of the population has an equal chance of being chosen
• Probability sampling - achieved through random selection methods like lottery or random number generators.
• probability sampling - provides a representative sample when the population is homogeneous and well-defined
• Simple random sampling - the most basic random sampling wherein each element in the population has an equal probability of being selected.
• Slovin's formula is a statistical tool to calculate the minimum sample size needed to estimate a statistic based on an acceptable margin of error.
• Systematic Random Sampling - a random sampling that uses a list of all the elements in the population and then elements are being selected based on the kth consistent intervals.
• Systematic Random Sampling - less time-consuming than simple random sampling and can be easily implemented with ordered lists.
• Cluster Sampling - involves dividing the population into clusters or groups, often based on geographic location or other naturally occurring divisions
• clusters are too large and there is a need for a second set of smaller clusters to be taken from the original clusters. This technique is called multi-stage cluster sampling
• Measures of variability, also known as measures of dispersion, quantify the extent to which data points in a dataset spread out or vary from the central tendency.
• • The range is the difference in the maximum and minimum values of a data set.

  • The maximum is the largest value in the dataset and the minimum is the smallest value.

  • The range is easy to calculate but it is very much affected by extreme values.
• The interquartile range is a measure of statistical dispersion, which is calculated as the difference between the upper quartile (Q3) and the lower quartile (Q1) in a dataset.

  • It is less sensitive to outliers compared to the range.

  • It is not affected by extreme values. It is thus a resistant measure of variability.
• Variance measures the average squared deviation of each data point from the mean of the dataset.

  • It is calculated by taking the average of the squared differences between each data point and the mean.
• Standard deviation is the square root of the variance and provides a measure of the dispersion of data points around the mean.

  • It indicates the typical distance between each data point and the mean
• Mean absolute deviation measures the average absolute deviation of each data point from the mean of the dataset.

  • It is calculated by taking the average of the absolute differences between each data point and the mean.
• The coefficient of variation is a relative measure of variability that expresses the standard deviation as a percentage of the mean.

  • It is useful for comparing the variability of datasets with different units or scales