CHI- SQUARE
Chi-square is a statistical test commonly used to compare observed data with data we would expect to obtain according to a specific hypothesis. For example, if, according to Mendel's laws, you expected 10 of 20 offspring from a cross to be male and the actual observed number was 8 males, then you might want to know about the "goodness to fit" between the observed and expected. Were the deviations (differences between observed and expected) the result of chance, or were they due to other factors. How much deviation can occur before you, the investigator, must conclude that something other than chance is at work, causing the observed to differ from the expected. The chi-square test is always testing what scientists call the null hypothesis, which states that there is no significant difference between the expected and observed result.
The formula for calculating chi-square (![]()
2) is:
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2= ![]()
(o-e)2/e
1. Determine degrees of freedom (df). Degrees of freedom can be calculated as the number of categories in the problem minus 1. In our example, there are two categories (green and yellow); therefore, there is I degree of freedom.
2. Determine a relative standard to serve as the basis for accepting or rejecting the hypothesis. The relative standard commonly used in biological research is p > 0.05. The p value is the probability that the deviation of the observed from that expected is due to chance alone (no other forces acting). In this case, using p >0.05, you would expect any deviation to be due to chance alone 5% of the time or less.
3. Refer to a chi-square distribution table (Table B.2). Using the appropriate degrees of 'freedom, locate the value closest to your calculated chi-square in the table. Determine the closestp (probability) value associated with your chi-square and degrees of freedom. In this case (![]()
2=2.668), the p value is about 0.10, which means that there is a 10% probability that any deviation from expected results is due to chance only. Based on our standard p > 0.05, this is within the range of acceptable deviation. In terms of your hypothesis for this example, the observed chi-squareis not significantly different from expected. The observed numbers are consistent with those expected under Mendel's law.
9. Degree of freedom formula (r-1)(c-1)
T-TEST
Chi-square is a statistical test commonly used to compare observed data with data we would expect to obtain according to a specific hypothesis. For example, if, according to Mendel's laws, you expected 10 of 20 offspring from a cross to be male and the actual observed number was 8 males, then you might want to know about the "goodness to fit" between the observed and expected. Were the deviations (differences between observed and expected) the result of chance, or were they due to other factors. How much deviation can occur before you, the investigator, must conclude that something other than chance is at work, causing the observed to differ from the expected. The chi-square test is always testing what scientists call the null hypothesis, which states that there is no significant difference between the expected and observed result.
The formula for calculating chi-square (
1. Determine degrees of freedom (df). Degrees of freedom can be calculated as the number of categories in the problem minus 1. In our example, there are two categories (green and yellow); therefore, there is I degree of freedom.
2. Determine a relative standard to serve as the basis for accepting or rejecting the hypothesis. The relative standard commonly used in biological research is p > 0.05. The p value is the probability that the deviation of the observed from that expected is due to chance alone (no other forces acting). In this case, using p >0.05, you would expect any deviation to be due to chance alone 5% of the time or less.
3. Refer to a chi-square distribution table (Table B.2). Using the appropriate degrees of 'freedom, locate the value closest to your calculated chi-square in the table. Determine the closestp (probability) value associated with your chi-square and degrees of freedom. In this case (
Chi square
1.
In chi square we find expected value
2.
First we make a table with real values
3.
And then write the column total down the table
4.
And row total in right side
5.
Then remove the main values
6.
Then we will calculate the probabilities by
7.
Row total/column total x column total/total x
total
8.
(Observed-expected)2
29. Degree of freedom formula (r-1)(c-1)
10.
Confidence level is generally taken between 90%
to 99%, but generally it is taken as 95%
T-TEST
A t-test is any statistical hypothesis test in which the test
statistic follows a Student's t distribution if the null hypothesis is
supported. It can be used to determine if two sets of data are significantly
different from each other, and is most commonly applied when the test statistic
would follow a normal distribution if the value of a scaling term in the test
statistic were known. When the scaling term is unknown and is replaced by an
estimate based on the data, the test statistic (under certain conditions)
follows a Student's t distribution.
T Test
1.
T test also
looks at the distribution of data
2.
Single sample T Test
3.
Independent value T test
4.
Paired sample T
Test
5.
Split file option in SPSS splits two categories
6.
to access go to data>split file
7.
paired smaple t test from spss
8.
significant value
Written By:-
Praveen Iyer
2013218
Group 8
Praveen Iyer
2013218
Group 8
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