I will not bore you with probabilistic definitions and formulas, which you may easily pick up at any textbook (or here is a good place to start)

Just think of this intuitively, random sample is a set of random values. In general, each one of the values may either be identically or differently distributed. $i.i.d.$ sample is a special case of random sample, such that every value comes from the same distribution as the others and its value does not have any influence upon other values. Independence deals with $how$ the values were generated

$i.i.d$ example: draw a random card from a deck and return it back (do this 5 times). You will get 5 realized values (cards). Each one of these values comes from a uniform distribution (there is equal probability to get each one of the outcomes) and each draw is independent of the others (i.e. the fact that you get an ace of spades in the first draw, does not influence in any way the result you may get in other draws).

non $i.i.d.$ example: Now do the same thing, but without returning the card to the deck (I hope you fill the difference by now). Again you will have 5 realized values (cards) after you do this. But clearly they are dependent (the fact that you draw the ace of spades on the first draw, means you will not have a chance to get in on the 2nd draw).

I will not bore you with probabilistic definitions and formulas, which you may easily pick up at any textbook (or here is a good place to start)

Just think of this intuitively, random sample is a set of random values. In general, each one of the values may either be identically or differently distributed. $i.i.d.$ sample is a special case of random sample, such that every value comes from the same distribution as the others and its value does not have any influence upon other values. Independence deals with $how$ the values were generated

$i.i.d$ example: draw a random card from a deck and return it back (do this 5 times). You will get 5 realized values (cards). Each one of these values comes from a uniform distribution (there is equal probability to get each one of the outcomes) and each draw is independent of the others (i.e. the fact that you get an ace of spades in the first draw, does not influence in any way the result you may get in other draws).

non $i.i.d.$ example: Now do the same thing, but without returning the card to the deck (I hope you feel the difference by now). Again you will have 5 realized values (cards) after you do this. But clearly they are dependent (the fact that you draw the ace of spades on the first draw, means you will not have a chance to get in on the 2nd draw).

I will not bore you with probabilistic definitions and formulas, which you may easily pick up at any textbook (or here is a good place to start)

Just think of this intuitively, random sample is a set of random values. In general, each one of the values may come from different distribution. $i.i.d.$ sample (is a special case of random sample) is a set of random values, such that every value comes from the same distribution as the others and its value does not have any influence upon other values.

$i.i.d$ example: draw a random card from a deck and return it back (do this 5 times). You will get 5 random values (cards). Each one of these values comes from a uniform distribution (there is equal probability to get each one of the outcomes) and each draw is independent of the others (i.e. the fact that you get an ace of spades in the first draw, does not influence in any way the result you may get in other draws).

non $i.i.d.$ example: Now do the same thing, but without returning the card to the deck (I hope you fill the difference by now). Again you will have 5 random values (cards) after you do this. But clearly they are dependent (the fact that you draw the ace of spades on the first draw, means you will not have a chance to get in on the 2nd draw).

Hope this helps :) Dig into it, probability is a nice topic.

I will not bore you with probabilistic definitions and formulas, which you may easily pick up at any textbook (or here is a good place to start)

Just think of this intuitively, random sample is a set of random values. In general, each one of the values may either be identically or differently distributed. $i.i.d.$ sample is a special case of random sample, such that every value comes from the same distribution as the others and its value does not have any influence upon other values. Independence deals with $how$ the values were generated

$i.i.d$ example: draw a random card from a deck and return it back (do this 5 times). You will get 5 realized values (cards). Each one of these values comes from a uniform distribution (there is equal probability to get each one of the outcomes) and each draw is independent of the others (i.e. the fact that you get an ace of spades in the first draw, does not influence in any way the result you may get in other draws).

non $i.i.d.$ example: Now do the same thing, but without returning the card to the deck (I hope you fill the difference by now). Again you will have 5 realized values (cards) after you do this. But clearly they are dependent (the fact that you draw the ace of spades on the first draw, means you will not have a chance to get in on the 2nd draw).