text: user285: I have condition as such mouse liver and spleen data. 
  So in liver I have WT (Wild Type) and KO (knock out) as well as in spleen WT (Wild Type) and KO (knock out).
  I have to compare the parasite load in WT and KO condition in both liver and spleen.
  In terms of data points what I got is this:
  
  10 mice for WT, 6 mice for KO in case of liver
  10 mice for WT, 5 mice for KO in case of spleen.  
  WT 20 8 21 9 22 10 23 11 24 
  KO 12 25 26 27 28
  
  
  So far what I know is I have to perform a parametric test.
  But I'm not sure if I have to go for a paired t-test or an unpaired t-test.
  Because here I'm comparing with between two groups, only so I don't have to go for anova if I'm not wrong. Secondly there is a difference in number of observation that is available for WT as compared to KO. So would that affect my choice of test as well.
  Suggestion would be highly appreciated 
  
  user2388: 
  So far what i know is i have to perform a parametric test .
  
  How did you determine this?
  
  But im not sure if I have to go for a
  Paired t-test or Unpaired t-test
  
  If you can't tell the difference between when to do a paired t-test and an unpaired one, you need to stop and talk to someone who can teach you basic statistics.  Throwing statistical tests at numbers when you don't understand them is a recipe for disaster.  
  
  user3848: Here's a combination of general advice and my thoughts about your specific data:
  If the samples are not matched, e.g. by time point, then it will be unpaired. You need a way to say sample 1 KO and sample 1 WT are uniquely matched to be paired, sample 2 KO and sample WT will be paired etc ...  pairing is great for a time series. If you have replicates within the paired groups then ANOVA is the way to go, particularly if the sample sizes are uneven.
  Note: in your case the sample sizes are uneven, you cannnot perform a paired T-test between two uneven sample sizes. HOWEVER you appear to a natural pairing, but I don't know whether this is biologically justified, i.e. liver and spleen. So you appear to have a biological pairing with uneven sample sizes. If this is correct it suggests a 1-way ANOVA is a better idea than a T-test.
  Parametric test: check for normality in each data set, e.g. 
  
  A Q-Q plot against the normal distribution (a cool way to do this) 
  Homogeneity of variances is Hartley's Fmax test (good for T-test and ESSENTIAL for 1-way ANOVA)
  68% of the data should be less than ±1 standard deviation around the mean, 95% less than ±2 standard deviation and 99% less than ±3
  Kolmogorov-Smirnov and/or Shapiro-Wilk test
  Skewness & kurtosis magnitudes < 1.96 respectively
  
  Note if you combine liver and spleen data sets and find a nice normal distribution, and one or both is not normal by itself, then you would need to consider the biological justification for combining the data sets.
  If the data passes all those tests, you are good to go. If not .... gets complicated,
  
  Normalisation, this is where you transform the data, in your case I'm not sure, but typically log transformation and then perform the checks for normality above. There are lots of ways to normalise data.
  Repeat the T-test/1-way ANOVA with normalised data
  Ignore it - yup its top level statistical advice, it is context specific and never a universally shared idea ... I suspect that is where the advice "just use a parametric test" came from. Please be aware if you do this, someone could turn around and sau "you've done it wrong". The statistical arguments about why this assumption can be valid get complicated.
  Use a non-parametric test, orthodox statistical advice, in this case it is the Mann-Whitney U test and Kruskal–Wallis test if you have paired replicates (i.e. non-parametric version of 1-way ANOVA).
  
  Non-parametric tests often lack power and I suspect for this sample size that will be the case. Personally I would try and ignore all this advice :-) - okay I would definately check for normality and investigate avenues towards normality - then I would perform a general linear model (GLM). The reason is that it is a much more powerful method for identifying whether the two gropus are different. Performing a single GLM with two paired groups is not trivial, so I would have to give serious consideration to 1-way ANOVA, or KW-test, which would be a cleaner solution - but could miss a critical part of the data. 
  
original_text: 285: <p>I have condition as such mouse liver and spleen data. 
  So in liver I have WT (Wild Type) and KO (knock out) as well as in spleen WT (Wild Type) and KO (knock out).</p>
  
  <p>I have to compare the parasite load in WT and KO condition in both liver and spleen.</p>
  
  <p>In terms of data points what I got is this:</p>
  
  <ul>
  <li>10 mice for WT, 6 mice for KO in case of liver</li>
  <li><p>10 mice for WT, 5 mice for KO in case of spleen.  </p>
  
  <pre><code>WT 20 8 21 9 22 10 23 11 24 
  KO 12 25 26 27 28
  </code></pre></li>
  </ul>
  
  <p>So far what I know is I have to perform a parametric test.</p>
  
  <p>But I'm not sure if I have to go for a paired t-test or an unpaired t-test.</p>
  
  <p>Because here I'm comparing with between two groups, only so I don't have to go for anova if I'm not wrong. Secondly there is a difference in number of observation that is available for WT as compared to KO. So would that affect my choice of test as well.</p>
  
  <p>Suggestion would be highly appreciated </p>
   2388: <blockquote>
    <p>So far what i know is i have to perform a parametric test .</p>
  </blockquote>
  
  <p>How did you determine this?</p>
  
  <blockquote>
    <p>But im not sure if I have to go for a</p>
    
    <p>Paired t-test or Unpaired t-test</p>
  </blockquote>
  
  <p>If you can't tell the difference between when to do a paired t-test and an unpaired one, you need to stop and talk to someone who can teach you basic statistics.  Throwing statistical tests at numbers when you don't understand them is a recipe for disaster.  </p>
   3848: <p>Here's a combination of general advice and my thoughts about your specific data:</p>
  
  <p>If the samples are not matched, e.g. by time point, then it will be unpaired. You need a way to say sample 1 KO and sample 1 WT are uniquely matched to be paired, sample 2 KO and sample WT will be paired etc ...  pairing is great for a time series. If you have replicates within the paired groups then ANOVA is the way to go, particularly if the sample sizes are uneven.</p>
  
  <p><strong>Note</strong>: in your case the sample sizes are uneven, you cannnot perform a paired T-test between two uneven sample sizes. HOWEVER you appear to a natural pairing, but I don't know whether this is biologically justified, i.e. liver and spleen. So you appear to have a biological pairing with uneven sample sizes. If this is correct it suggests a <strong>1-way ANOVA</strong> is a better idea than a T-test.</p>
  
  <p>Parametric test: check for normality in each data set, e.g. </p>
  
  <ul>
  <li>A Q-Q plot against the normal distribution (a cool way to do this) </li>
  <li>Homogeneity of variances is Hartley's Fmax test (good for T-test and ESSENTIAL for 1-way ANOVA)</li>
  <li>68% of the data should be less than ±1 standard deviation around the mean, 95% less than ±2 standard deviation and 99% less than ±3</li>
  <li>Kolmogorov-Smirnov and/or Shapiro-Wilk test</li>
  <li>Skewness &amp; kurtosis magnitudes &lt; 1.96 respectively</li>
  </ul>
  
  <p><strong>Note</strong> if you combine liver and spleen data sets and find a nice normal distribution, and one or both is not normal by itself, then you would need to consider the biological justification for combining the data sets.</p>
  
  <p>If the data passes all those tests, you are good to go. If not .... gets complicated,</p>
  
  <ul>
  <li>Normalisation, this is where you transform the data, in your case I'm not sure, but typically log transformation and then perform the checks for normality above. There are lots of ways to normalise data.</li>
  <li>Repeat the T-test/1-way ANOVA with normalised data</li>
  <li>Ignore it - yup its top level statistical advice, it is context specific and never a universally shared idea ... I suspect that is where the advice "just use a parametric test" came from. Please be aware if you do this, someone could turn around and sau "you've done it wrong". The statistical arguments about why this assumption can be valid get complicated.</li>
  <li>Use a non-parametric test, orthodox statistical advice, in this case it is the Mann-Whitney U test and <strong>Kruskal–Wallis test</strong> if you have paired replicates (i.e. non-parametric version of 1-way ANOVA).</li>
  </ul>
  
  <p>Non-parametric tests often lack power and I suspect for this sample size that will be the case. Personally I would try and ignore all this advice :-) - okay I would definately check for normality and investigate avenues towards normality - then I would perform a general linear model (GLM). The reason is that it is a much more powerful method for identifying whether the two gropus are different. Performing a single GLM with two paired groups is not trivial, so I would have to give serious consideration to 1-way ANOVA, or KW-test, which would be a cleaner solution - but could miss a critical part of the data. </p>
  