Other than understanding the differences between population and sample, you should also know that there are differences between the population parameters and sample statistics. We assume that the sample is representative of the population, and possesses the same characteristics as the population. Hence, we choose a small group of participants to be representative of the population to undergo the study this group of participants is the sample. However, it is impossible to to study all the members of a population for a research project, because it just costs too much and takes too much time. A population refers to all members of a defined group that we are studying or collecting information on for data driven decisions. Make sure you know the difference between a population and a sample. This is one of the important things that I would highlight to students. Best way to find out? Use Excel to plot your distribution out and check if it looks like the picture above.Īlso to understand the shape of a distribution better and to differentiate one distribution from another, we need to know its central tendency and spread. In statistics, we often assume that that our means are distributed on a normal distribution this might not be the case all the time but more often than not, just an assumption. This means that you will get what we call a 'normal distribution', also known as the bell (or bell-shaped) curve. Central Limit Theorem (CLT), Normal distribution, and the Bell (-shaped) CurveĪccording to the central limit theorem (CLT), the mean of many (or rather 'a lot' some say at least 30 as a rough minimum) random samples independently drawn from the same distribution is distributed approximately normally.

This post will also revolve mainly around descriptive statistics.Ģ. If you have issues understanding descriptive statistics, you might face more problems as you go into inferential statistics.

I would recommend that you read up and understand descriptive statistics well, because it forms the basics and foundations as you proceed into inferential statistics. Statistics is divided into two types, descriptive statistics and inferential statistics.

So in this post, I will go through what you NEED to know in statistics to help you understand statistics better and get through the 'terrible' (or fun) times that you will be expecting.ġ. Psychology students who feel that statistics is difficult often give several similar reasons (like disinterest in Maths, not understanding probability, just bored with the formulas, etc.), but I believe statistics in psychology can be quite easy once you have a good grasp of what is required (the foundations) and the logic of statistical analysis. But it is still a module in psychology that you cannot avoid and would have to enroll in for sure. Statistics is one of the topics that most people doing psychology either have a love or hate relationship with, with most more having the hate relationship.

Statistics made easy