Sometimes you can’t wrap up all the information and definitions of a certain topic or tool in your head.
Imagine the scenario that I’m sure it happened with a lot of us where someone came and started asking you about a certain definition, and once you hear it, you may think that you’ve never heard of such a thing before, but it’s totally the opposite and you lost half of the mark!
Today, I’ll walk you on some of the statistical terms that most probably you’re using but you don’t know the name of the main umbrella covering it.
Some of the most commonly used terms in statistics which are Mean, Median & Mode.
I was one of the people using those statistical terms without knowing that they are called measures of central tendency.
They are 4 moments:
- Skewness (also known as asymmetricity)
- Kurtosis (also known as degree of peakedness)
You can check more about this title here.
A directional hypothesis is the case of which the researcher indicates the specific direction that he/she expect the relationship will go through in a study. Example:
> A researcher might hypothesize that employee A will perform better than employee B in a certain task.
A non-directional hypothesis is when there is no specific assumption about what direction the outcome of a study will take. In this case, the researcher is looking for relationships without knowing what he/she will find by the end of the study. Example:
> A researcher might hypothesize that two employees working on the same task will perform differently, without specifying which employee will perform better.
Each type has its own use based on what a researcher is doing and aiming for.
Primary Research is when you’re the one collecting, formatting & coding the data and creating a data dictionary if needed. This type allows you to have more control on the data quality. An example of that is when you design input forms accepting a certain datatype so the user won’t insert a wrong value in the form fields (entering his age number rather than just choosing his DOB from an input form having a date datatype).
Secondary Research, is the approach where the researcher will rely on already existing data which was collected previously. This approach has its pros and cons, it may help you in saving your time since data collection is the most time-consuming task, but at the same time you will have the data quality risk of the previously collected data.
To make this straightforward, I’ll explain the difference through two examples.
It’s when the user can play around with the visuals colouring, time ranges, choosing what categories to show, etc…
Full-Service Graphs are the ones showing the full output or result without allowing the user to change anything.
Dichotomous variables are binary variables with two possibilities like male or female, rich or poor, etc…
Polytomous Variables are the ones with more than two possibilities. For example, a rating of bad, good or excellent.
We all use some terms in our life but we ignore the main bold titles of them. Sometimes this way of doing or learning things may not effect you, but imagine you’re in an interview where you mentioned in the resume some skills or a piece of knowledge you have and the interviewer asks you some general questions based on what you’ve mentioned in that resume in order to see how deep the water is, and you failed to answer since you only focused on the minutiae not also on the main important titles.
I hope you enjoyed this article and you gained some info out of it. Please don’t forget to clap and share it with your friends.
Thanks for your time and let’s boost our knowledge!