Everybody has probably already realized that there is almost no data that we cannot get. We can get data about our website by using free tools, but we also spend tons of money on paid tools to get even more. Analyzing the competition is just as easy, competitive intelligence tools are everywhere, we often use Compete or Hitwise. Opens Site Explorer is great for getting more data about our and competitors backlink profile. No matter what information we are trying to get, we can, by spending fortunes or no money. My favorite part is that almost every tool has one common feature and that is the "Export" button. This is the most powerful feature of all these tools because by exporting the data into Excel and we can sort it, filter it and model it in any way we want. Most of us use Excel on the regular basis, we are familiar with the basic functions but Excel can do way more than that. In the following article I will try to present the most common statistical techniques and the best part it is that we don't have to memorize complicated statistical equations, it's everything built into Excel!
Statistics is all about collecting, analyzing and interpreting data. It comes very handy when decision making faces uncertainty. By using statistics, we can overcome these situations and generate actionable analysis.
Statistics is divided into two major branches descriptiv an inferential.
Descriptive statistics re used when you know all the values in the dataset. For example, you take a survey of 1000 people asking if they like oranges, with two choices (Yes and No). You collect the results and you find out that 900 answered Yes, and 100 answered No. You find the proportion 90% is Yes 10 is No. Pretty simple right?
But what happens when we cannot observe all the data?
When you know only part of your data than you have to us inferential statistics. Inferential statistics is used when you know only a sample (a small part) from your data and you make guesses about the entire population (data).
Let's consider you want to calculate the email open rate for the last 24 months, but you have data only from the last six months. In this case, assuming that from 1000 emails you had 200 people opening the email, which resulted in 800 emails that didn't convert. This equates to 20% open rate and 80% who did not open. This data is true for the last six months, but it might not be true for 24 months. Inferential statistics helps us understand how close we are to the entire population and how confident we are in this assumption.
The open rate for the sample may be 20% but it may vary a little. Therefore, let's consider +- 3% in this case the range is from 17% to 23%. This sounds pretty good but how confident are we in these data? Alternatively, what percentage of a random sample taken from the entire population (data set) will fall in the range of 17%-23%?
In statistics, the 95% confidence level is considered to be reliable data. This means 95% of the sample data we take from the entire population will produce an open rate of 17-23%, the other 5% will be either above 23% or below 17%. But we are 95% sure that the open rate is 20% +- 3%
The ter dat stands for any value that describes an object or an event such as visitors, surveys, emails.
The ter data set as two components observation unit, which is for example visitors and the variables that can represent the demographic characteristics of your visitors such as age, salary or education level.Populatio refers to every member of your group, or in web analytics all the visitors. Let's assume 10,000 visitors.
sampl is only a part of your population, based on a date range, visitors who converted, etc. but in statistics the most valuable sample is considered a random sample.
Th data distribution s given by the frequency with which the values in the data set occur. By plotting the frequencies on a chart, with the range of the values on the horizontal axis and the frequencies on the vertical axis, we obtain the distribution curve. The most commonly used distribution is the normal distribution or the bell-shaped curve.