Question

What are 2 concepts of statistics hard to understand?

Answer 1

Terminologies In Statistics – Statistics For Data Science

One should be aware of a few key statistical terminologies while dealing with Statistics for Data Science. I’ve discussed these terminologies below:

• Population is the set of sources from which data has to be collected.
• A Sample is a subset of the Population
• A Variable is any characteristics, number, or quantity that can be measured or counted. A variable may also be called a data item.
• Also known as a statistical model, A statistical Parameter or population parameter is a quantity that indexes a family of probability distributions. For example, the mean, median, etc of a population.

  

1. Quantitative Analysis: Quantitative Analysis or the Statistical Analysis is the science of collecting and interpreting data with numbers and graphs to identify patterns and trends.
2. Qualitative Analysis: Qualitative or Non-Statistical Analysis gives generic information and uses text, sound and other forms of media to do so.

For example, if I want a purchase a coffee from Starbucks, it is available in Short, Tall and Grande. This is an example of Qualitative Analysis. But if a store sells 70 regular coffees a week, it is Quantitative Analysis because we have a number representing the coffees sold per week.

Although the purpose of both these analyses is to provide results, Quantitative analysis provides a clearer picture hence making it crucial in analytics.

Categories In Statistics

There are two main categories in Statistics, namely:

1. Descriptive Statistics
2. Inferential Statistics

Descriptive Statistics

Descriptive Statistics uses the data to provide descriptions of the population, either through numerical calculations or graphs or tables.

  

Inferential Statistics

Inferential Statistics makes inferences and predictions about a population based on a sample of data taken from the population in question.

Before we move any further, let’s define the main Measures of the Center or Measures of Central tendency.

Measures Of The Center

1. Mean: Measure of average of all the values in a sample is called Mean.
2. Median: Measure of the central value of the sample set is called Median.
3. Mode: The value most recurrent in the sample set is known as Mode.

Using descriptive Analysis, you can analyse each of the variables in the sample data set for mean, standard deviation, minimum and maximum.

Measures Of The Spread

like the measure of center, we also have measures of the spread, which comprises of the following measures:

1. Range: It is the given measure of how spread apart the values in a data set are.
2. Inter Quartile Range (IQR): It is the measure of variability, based on dividing a data set into quartiles.
3. Variance: It describes how much a random variable differs from its expected value. It entails computing squares of deviations.
   1. Deviation is the difference between each element from the mean.
   2. Population Variance is the average of squared deviations
   3. Sample Variance is the average of squared differences from the mean
4. Standard Deviation: It is the measure of the dispersion of a set of data from its mean.

So far, you’ve learned about Descriptive statistics, now let’s talk a little bit about Inferential Statistics.

Understanding Inferential Analysis

Statisticians use hypothesis testing to formally check whether the hypothesis is accepted or rejected. Hypothesis testing is an Inferential Statistical technique used to determine whether there is enough evidence in a data sample to infer that a certain condition holds true for an entire population.

To under the characteristics of a general population, we take a random sample and analyze the properties of the sample. We test whether or not the identified conclusion represents the population accurately and finally we interpret their results. Whether or not to accept the hypothesis depends upon the percentage value that we get from the hypothesis.

The probability and hypothesis testing give rise to two important concepts, namely:

• Null Hypothesis: Result is no different from assumption.
• Alternate Hypothesis: Result disproves the assumption.

*** however this are the above are the basic idea about statistics & PROBABILITY is also must included with this above in INFERENTIAL statistics most of cases are depends on the basic rule & assumptions of the probability. however as the question menteioned here about only on hard concepts of statistics , i have briefly describes the simple way to understand & learn the statistics.